{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The Grand Poobah Classification Notebook\n",
    "In this notebook we will cover uses of scikit-learn for many different purposes including:\n",
    "- Cross Validation and metrics for scoring (including ROC and confusion matrices)\n",
    "- Pipelines\n",
    "- Common (nonlinear model) Classification:\n",
    " - K-Nearest Neighbors\n",
    " - Naive Bayes\n",
    " - Decision Trees\n",
    "- Bagging (Random Forests)\n",
    "- Boosting (Gradient of Error)\n",
    "- Statistical Comparison Questions\n",
    "\n",
    "We will look at some of the advanced features of scikit and look at many different ways of going about using the system to its fullest potential.\n",
    "\n",
    "- http://en.wikipedia.org/wiki/Grand_Poobah\n",
    "\n",
    "For this assignment we will use the LFW faces dataset and try to perform recognition of different individual's faces with several different classifiers. \n",
    "\n",
    "You can look up the dataset here:\n",
    " - http://vis-www.cs.umass.edu/lfw/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# fetch the images for the dataset\n",
    "# this will take a long time the first run because it needs to download\n",
    "# after the first time, the dataset will be save to your disk (in sklearn package somewhere) \n",
    "# if this does not run, you may need additional libraries installed on your system (you will need to figure out which ones!!)\n",
    "from sklearn.datasets import fetch_lfw_people\n",
    "\n",
    "# only load people that have N or more examples in the dataset\n",
    "lfw_people = fetch_lfw_people(min_faces_per_person=20, resize=0.4) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Size of the dataset: (3023, 1850)\n",
      "Number of unique classes: 62\n",
      "['Colin Powell' 'David Beckham' 'Donald Rumsfeld' 'George Robertson'\n",
      " 'George W Bush' 'Gerhard Schroeder' 'Gloria Macapagal Arroyo' 'Gray Davis'\n",
      " 'Guillermo Coria' 'Hamid Karzai']\n"
     ]
    },
    {
     "data": {
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4aIjtRwK3jy8cSZLU7Vq5rPNh4BsRsQdwA6XA+QvgFcBebYxNkiR1oVbWObk8Il4LHAf8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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x124c07240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print ('Size of the dataset:', lfw_people.data.shape)\n",
    "print ('Number of unique classes:', len(lfw_people.target_names))\n",
    "print (lfw_people.target_names[10:20])\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "import numpy as np\n",
    "%matplotlib inline\n",
    "\n",
    "plt.hist(lfw_people.target, bins=len(lfw_people.target_names))\n",
    "plt.xlabel('Class value (one per face)')\n",
    "plt.ylabel('Class frequency')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 102.33333588,  133.66667175,  143.66667175, ...,  175.        ,\n",
       "        148.        ,   47.66666794], dtype=float32)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lfw_people.data[25]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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6Ua6yi2ZCGZwMvhAGGwOOAe2A0QKeTmdkwNNn6Pmxspag44ZER0fs\n4Vy8YL0MdHGsSqrlZZYrA9oYURDo8+NY99kEvANdKAu3P0vdMwiizx3osn5Q11zr7kCn4YEqMhsO\nl4hRwPGYOabrBV5rXlHLvJNMx66RWjl2MRUILlUdHVKLedTKOrdTFV6Pa+IAx4rTw3TaXmX6iGnZ\nQHHfhSjgFHQZ8GoMz3t+Drv0HIfrC2mZmZ2ryc/jfqsx0Rjg9bBcDXB3AnTa4RwfBEDY5WSlUxbj\nMt1cU+YyOoVlS8znGeTxrMzdaVnMbEDV+GifRPsZUMru8YcemkSI8qNeZ2dnOD4+XtpOTk6uKGOM\nD/eDSoBDDVnUN8AX92ofq8FSwGcgGrP1upXXARrLWoMujtkSZn51dBgzgK5MUVAAy64ag5lBrZJZ\nfQVezUVhBe9RinhuTdzzeAEyAyWLBaM+s9kMR0dHiy1AV0uVu35hY8B9zoYgPJuaMXRt7QWdYztm\n/B6wuXb2jImTxwJ0DDR2BXsUFbgaZ+jgsmvG1zsw8T2uzi3DoEzHzNRSHC3fPU/L5/9kD+VW15T7\nLLajoyMcHh4u9rPZ7EoaXfuE+ya2eK72t/7PGwvHa5knwh6J1r8XeO4+515nzLYq8NYedAw0dqv4\nPDMcu5chmfsX32m8pGynojGdxoIOdDyoGSh7gOcUI3ODAGA6nS6+n06nV0ARddeJ4mC6w8NDPHjw\nAEdHR0vrD7PV9Q5QLcCpR6L9zHqh7NfLcLV+i753/Zu5kTUWbslagi4ssnMpNebKOlFBxOXw6gpm\nuQx42vEcMwZTAPkKGp33YYDU0tbZeXXzOCGideUyuP6a9dSFvQy4+/fvL1xM/hEnlxnP5v9939nZ\nwXQ6Xeor/avqqLsqb8ZsDDxt3yrAcxPdmdfA9eJ6jpW1BB0zhrIcf6f73o1B7QAXou4lJ2HOzx+6\nuJptywDnBtIpBbtx+moAfX0A79Utn0wm2N3dvbLpivkAHW/379/H/fv3l0AXgIu9gm4ymSxAN51O\nF64lu7hxfnt7G9PpdFH3XtDxmKjnUwNe5l5mOqL1UHdXPYUxstagi+OerfdaVhR2T11mzNWBl5Px\nnuuduZdqRTMrHNY3ADebzRbHzDQKFJXJZIK9vT3s7u4u7Wugi/MButgCdAw8tv4h0+kU0+kUu7u7\nmE6nS+wX38Xnk5MTTKfTK6BzyRgeCx6jFuBaQHR6oX3ogHbnQKfA6GU5PnZuqYKEy3cDH6JzfGz1\nWAlqoOthOQVpgOD4+Biz2WyxhfIrE3JdgLkbube3h729PRwfH2Nvb29xfYDPMd3JyckV0B0eHl6Z\nRlBXj5k16u9AN51OrxgOFlV0DRfGsFzNtdTveAyjHhm71VYTtWQtQZcxnX4ey3jA8huJAaSujdZH\ngVdzL2OfxRQOdHys7uXx8fFS+j4AoOyn9d/a2sL+/j729/evKLmuxNBfR9+/fx+vvvpqN+hii3I4\nzmTQBQOGa8nupcbruogg+pXBcJOupRPuyzC4mv29U0yniZQa28Vxza3M4jaeB3NTB+xaZoDL2LcG\nOAWmApYnsONNVMp4DDwWVmAGsytX3cuTkxM8ePAAh4eHiy1zL0PxMjes56cxkX3munL/hk4w00Ub\ne5nNufY1wGXsxW19XTBdL7PxuRgs7VgGtstYssXVurHV4/rq3imC1pfPh9TiB11hEvdzap6V8uLi\nYhEfcsYvA12cD5Dx78yAh1MC/LoGVj7OXuofqriVQmN0IT6rMewxbjX90b6viYv97wTT9YDOfVe7\nnr/XTnPAC8XM6seA42NlK2YYVxdOsLj6ZADM/piDP/MzYl5NmS7OK/swkzILTybLv6tTq8+JEgXc\nKqzg9IL1o9UHPZtLoDGwnVF0+15Za9Axq9RAFPvapu6JA118DsXk81wW10l9eq6bW2KkddZAvidb\npsDTNuo5nvhlwAU4+LvYc7KGpyPUlayBjl/dsAroehikFcfV9ENFdSKEk2+vS6bTc3x91sEcG7By\nx547mAGXuZc1puNr3AJkHSBWDK6PA53+FVecC+F4lJWP6xFxlq4YUcXlxIu6lwAWi5QVdA5wfC33\nvfZpjz7o5zGMlukNixoTBpvuV2XttQRdS1ZtLAPOMaAG8Q50zHJ8LZevLKMxnasTlx1giIyfrg0E\nsFjNwXGYspXGkPwcjveYFbieDOqdnR3LcI7pgu0iWxnTFpG5dCyoDNo7zi1g6bi4/ohnaXtYV5TR\naozZkrUGHXe6WpZs8N21Ko4ROeGiWcnaoPK1rkznQmZtjYEOhdV7OInBP7VhUNZWzbs6OOYPAHC2\nNp6viRMFCwNuZ2cHu7u7i2mLHvBlwGM3P3Pp3Lhn3hBf74yH6lKPLvTKWoJOO25VsOn3ITU3tAY4\nvp8/O0bRMp3Cs/JwXd3K/ABBKHQwm24OfNkLdXhzc45OIRl4DnzsXkZd9/b2LOim06l1mTPQtQCn\nUnMj9V5nQFy5dxZ0IQ5QtU2vc+LAwyzXigGyga8BTp/D97j66sr8UAh22dyyMmW4yFrqrwP05znx\nbHaVOfOoe43TdMqA3eOdnZ2lJWgZ0/Fr+zKlVyOlLN2SGtu5BFFrfG8FdKWU9wB4FsAfuzz1mwCe\nH4bhU3TN8wDeDeBNAD4L4NlhGF4a85yM6VznXse9jGN2oRwDtFiPXZ+We8nXuzYAD5edxXcMOJ5b\n42dG3Rl0vKKFJ7Vji3t4H+3VBcqaGHEg4e84URNLw8JgMOi0LJflrLFYS3qB4Vg9+kUNJZfr4vSW\njGW63wfwHID/E8AEwI8A+EQp5U8Nw/ClUspzAN4L4BkAXwHw0wA+XUr51mEYjn2RVyUaHMct5mq5\nQ5w4YGkBc6wPn7FjPIuZjZ+v9WbQ8fc6VcDlK7jZtYxfFsSqFl5EfXx8fOUHnAE6Tf9nbqDWXTde\nc+mApn0Yewe8HrD1hBiqJ5xQ43rVDP6qMgp0wzD8PTn1/lLKswC+C8CXALwPwAvDMHwSAEopzwB4\nBcC7AHy89zkOdE4cAFrup0rLZ+e5L3ZBa/XJwFlzhfm8TgWEkp+dPXypT5Yxde4mb8p2DDoGrfsp\njiqqa4e6oxyHxr4GOh1XB5Zelmt5TG4sskTKTcrKMV0pZQvADwO4B+BzpZQ3A3gawGfimmEYvl5K\n+TyAt2FF0DnQ1BTfKTIznYuvMvewJ8Zz9WqBLqunc6sC8MxyPFnt9tpO3tTlDNBpksW5ltxXrj0x\ndhr/aXJF/2SE618bU+7zMcCrgYbrn8WUNw280aArpfwJAP8YwD6AfwXgh4ZhGEopbwNwgTmzsbyC\nORi7pcZ0Lh5Tq+gAV3MvVJzfrqBt3ePcS/dsrSu3mV2f8/PzK0u/XH+4+vA+XMwMdAE8BYm6vK7P\nue46gc+slzGdGgw3Nr2A62E5FhefZnW4rqzCdF8G8O0A/giAvwDgY6WU777JSrWYDsgtY6972XJr\nbiKmc9e16sf14oFXANb6odbW8/PzK4kVBzrOQAZAevpAjZ0Cr7YkzPVfj9KPdTfVAGZ153N67XVk\nNOiGYTgF8DuXH79YSvlOzGO5D2GeXHkKy2z3FIAvjnmGgi72rgMyoOi9nA7XZ6hkrmSNvXpdUnef\nPjurg7s/Y/DsOFibYy03oa7TBMEwnIV1/ZO5yVnb2M0PYLuMYJYMc/1Y6281dHpdBszX1L00sgVg\nbxiG3y2lvAzgHQB+AwBKKU8CeCuAnx1V4NbVt3nx3kkv860iOqAuDhnDijrQrIz6uQY4FedSOyXb\n3t7G6enplUl2jgE1hR/10YSLAtHVSduugFOQcTaxx/hp2bW+qemCM+wZ+K4DwrHzdB8E8PMAfg/A\nGwH8ZQDfA+D7Ly95EfOM5kuYTxm8AOCrAD4x5jm9lhzoX23g2M7dn4GmxnQtwNVcJQc0rVsP8LiN\nunJE45SI2RyAnKcQkq1m0TK0zjVXPgOeKnjNIxgr3BfumWygrmuwnYxluj8K4OcAfCOAP8Sc0b5/\nGIZfBIBhGD5USrkH4COYT47/EoB3jpmjA+qunwNcy8pyp2UuTM1taTFdDXxaT1fHFhvqs/S53Da3\naoQBqPXMlrC59tTWesZxD8vrxsvQQhzbufGo9bvr616m62HCVWXsPN27O675AIAPrFgfAKtTt+t0\nx5QAliydsk2tfD5exd3tuS4DHEtmUFzKXhmP+4mBmO0ZGPHzIT7WnxQ5IMd3NdZTo8Dtywxbb79q\nf9VAVSvTPXss26712stMxoJSB82V8ahcCQV27Rp3T03UMrt0vZt74vuzurF7BTx8KW98535VX3PF\n9TkKfGcItM5qKLWcrD5Zf7m+0GPHor3Mmslagq6m9LXAvNUZLTD1Ws4MQC22HFO+U9osiFfg1ZZq\nuWdm4MvamAEuNgUWl10D2pixa7muNdG+aLn4zPYa/64iawm660rmjrVcwriO97chLeud1VuTJLrp\nKpJaH2RslzGCeg8cKzum0Fi0xnDOtawxU49oWdon2hd67AzCnWK6THqYquaa9QCvx6+/CUDWBpaP\nM4ZVS+3AlymrsmcNcBwH8nOVzYIBHOC0jVHPjOHclNEY90/7N2uX6xc1ENkz7px7mUnNSvPWO4e0\nijiLqXGH/hjUASyruwNePJeVMVOwHoVw5ev3rm2aqdTXSLiph1o9GfDu+ni+urNqDGpMpffpsQNZ\nrS9rY9Qrawk6bUQPsziGYGVwq1zGSBbf8BZZO7XE7jirazagnFxwWT5Xnqu/Yx6tkx6HUropgtpc\nX0tZo8+Y7Wp9zP2g0ss6GcNlDN161p0BnQoPvp6vKZ3u2TpmbmSP66mbLo7lOacsZlBgZRPMqhA1\nBdU+cADKjECLLRzoMsBl3+lCbWUrrWOwnHMv3S9Hsv5QUYbNmM71Z82Q9Mpagi5jOu4gVsaxHdOb\nTOm5RoPy7PsMBL2rOrjcVvsyttNnZ89w33Ede15+5EDIdeKlZrHWU9uZuZdZplONVa1tWSzb6n/3\njDsJupBWIqTFctd1MV19HPi4rq6OWlentHytS5qoUrMBYoXmcsZIBhwHtszlzMrg/qklrmqxs/ax\n67Pa5hjOGa4WuFeRtQQdW+gsceLOZYDj77IVGDchysgt45ABL1MgdpG5LF4yxfXIgO+Y2dXRGYRa\nHFe7jjetQ+bm67/5aLsyL6bXoCqQVF8yFmvVvSVrDTpnDZ31dkqs+xrYtGNrbOpEldu5mVq+c7tC\nWbVeGaMG4ByrtVxUjkedZO/R5H1m/HqSKto3DjhqaPRc5j5n7JaJM87ZJLiOw50BHSvMGJ+7B3i1\ne/WZNYXJRNmOz2VlOibgOtXcWE0QufhJRddkat0BXPlrZbfIme/JFDAziFnfKcM50GmMXDN4zmC3\n6pZ9duXeGdCpe6nzU7x35zPARbmcbbwp91KZpuXCZe5bDXSshI4tAhAMDldPt1RMRf+ZNQNd7adE\narycErcYLgNdTeGdkXKA47o5A1gzsncOdKwsOj+lHeH8cj7mztSsF4sqcc3FHAPUrE5ZosFdH8rH\nK/xdGx07aV/1gC7Kcn/iyEoZZemrGPQXDmoAM2GXV0FXGzu+X8uJNjvWc4a6tq2qAyprD7posJuX\nUYXKjse6iTXAuWfwc1qfe7eWpdX4KsCm7OQA7H4Vru1iALtXuEdZ/KIh/TPIeOuX9lfGWgE2BjV7\nKW6qQN3J2Osys8zIOJZrGcHsc6+sJejij9/VjWLXygForJKzOIZr3ZdZwOuATQff7QNoCjK3uTo5\ny6+K5F5WpGywteVfr6cvqc0Apv9jEH/lFXsGIQNPx5p1RZ8R19Z+caEAy1jP9dMqjLeWoGOm05ec\n9nRGSC/oeMD43l5/Pev4sYzGoHKpenbz+L/Gsy3+ZdX1jbpZfE2W+te6u7c385+D7O7uLthO3U9l\nRwWegk2ZzomL49jFdIvAa2NR0zXVszGylqBjpuNsVs9KhBBnkdy9DDi3X8Wa9bBdq26s8I7Njo+P\nMZvNMJvNcHR0tDjm8/yXx64dzHIt8Ls2BasFwPj/6OI/C3Z3d6+4nsyGUSYznj7bjbuOuXoq7K62\nflvYC7ja+I2RtQdd7LMBcFMBLaXmdLcLrjPmc8cq7rssk8bszbEZ/wtqxmgBLN70T0LiX3kyReU6\n97CwSrxVLOoYTDebzZYYT11O/iORvb29JXeTQdQTV+s1tTHM2tzjWo7tm5qsPeg429bLXnxNHHPW\njMtScUzHZdbuqbGbUw5lmmC1GrCY1ZTZGAD8V1iZQcrqWrPkmqwIsMU2m82WgKbuZ/xfXSR5GHBu\nDm/VtHyP1EDUo2vsBYyRtQcdW8DezuixUoD/mYizkr0s13NNzcWJuO34+BhHR0c4PDzE4eGhPWbA\nMbPptEENcC2A1QAwmfh/6OG3Qsex/lVWGIMYg7hOM6NZv7VEDWdNWrrSc82dAx3/S80qjQ+QsRvH\nrxbQFRnMTDUF7BlMFc3GOvcy2Oro6AgPHjzAgwcPcP/+/cXx4eEhHjx4gKOjo/Q/CXQCOwOW+8x7\nTcdrMsLNy2X/T8dxXiTKAnBxTucVHxXLZYZ4DMg03h0jjwXo+J9qHIhqgMwA5xSMwabHfO3YTq5J\n1Iv/TSfY7MGDB3j11Vfx6quvLkAXWyRK4p6TkxOb/HD1zqy59kfs1UC4pFaci+wjeymcCIqxDcBN\np1McHx8v/c/6KvGSY0d3jfaBrgTqAdzrgul2dnYWlpBXqChTcCe6aQbNSDLz6FyQxoB83oGxB5DO\nUnIMp+x2//59vPrqq7h//z6Ojo4W7BYT3+yKaXZO28B1aNU5ylR3srbcy7FSPD/mC7lsjfk0yRJj\nXVNqNRA9TJVNx7iF5o7JXFl3AnQxSGwpA3j81uJah+o8DQMtRDsrA14tba0DwuIGQ+sa2cpIkjjA\n3b9/fyl5EozBoItEhKbKndTqHOKYjgGXrdl0/cOgiOmfLMkSbYuxdqtCau3J6uD0RBcA9LRFz98Z\n0DHTMcvFFlMICio3t1MDjAu0FXhadg/4nGSDHm7XbDZbuJQOeOxG6hIvBkQ8o5ah7RHHYgo6bRuA\nJQbWVHy0l0Hn/o+cFwHo/+LxM2v9zNdoXRzj8Vuq3Zgp4O50TMcZudqkqQLNgdIxUwt4/LlmPVl6\nAcixnDJd7COe0wXIUfeM0VsMpsd6Tl1LBhuDzsVfzv3ipV0KuthzBtZNzPeCr8VwGoqwMef2Zux2\n55mOrd7p6emSe+kSKD1slAEvGAKoA60ltVR1xnYc2ymrcZLEgW1V0NU+twBX8zB0BUhP2/UXDTHW\nWaJM61vrb30+g8WFJNHHcY9jSTUqY2QtQafvzNeflzjAAT5JwOf1GiccC9UYskcyd8XFE7qqX98p\nGQI7YF0AABq0SURBVIrAsZS6e84y1+pTq3MNdPzPrFnS6vz8fGEguRxWZtcX2v5s7Wetr9XoZYzl\nys0MRYs1x8hago7Tysp0vIA265AWu6nwNdmSMv3c6uhMwVvAc79di2eF8oc4d6+XkXsU12Uu+R9a\nuR1xfRgtXdLlnsH3K9A4eeb6QuvYkpp+MNOpaFzqtjsBumA6VkwNrqPBIRnbZUzIez2XuZYqYzo7\ns5I1F4uVjdkt9hno+HlZXVriQMeAC1CxVwLArh5yZXM/1Ni+N7bjOjim0jFwY8GsrvfX7mM97JG1\nBJ0ynQ6GppIzdmPJGMopaStpMtayObYMZXPpa02T1zKIDAZthzMqtWMWl7nU1SasqAG+Wj9pe+Kc\nGqHst3yOVbJkUCsTWQOyCzGU8bTeY2QtQRfzdDH/pL9I5iVGvAKCs44tpguFaYHJXZMpdiYuExht\niAXATzzxxGLwYtnU/v4+7t27h9lstlTexcXVVxBkCYXM0vNnF8fpJLjr/2A6DQF4izZxHXd3d7G/\nv4+DgwMcHBxgf38fTzzxBO7du4f9/X3s7e0t5u7CoLhYK+vjrA+csdMkEPeDHt+EAQbWFHTHx/N/\nS46lQrqYNla2q/vFnddiwuiszNppUqBmuVWyOEOTEgG6/f39pfWIoZD37t1b/Kogy5i1Bl0VLgOg\nW9HiXEo2guoeu+MYDy4z2hyGhQF4cHCwmEZQ0GVtU1C6hI1zCZ3r6lxULccZrjGylqBTpmPAxUDw\n4OrEcIvl3OaC9NiPsXCcOau5PqGAOzs72N/fB4AFCGez2RLgYjGzKjMD0dU92qvX6XHNbVWg8f78\n/NzGohqT8YuLmN1jC+YLEAbTxX0KqEw4mcOSubDah5r17AXeWFl70Ll3cOzs7FwJtDM/PXOv1NXU\nLGHmfo7tcJcRVKYDsMQA+soFnbPjpVKsQPyMeG5m1XnvkjLuJzp6fHFxsfTbvVgpw+C7uLiw71Dh\nFShuVQo/x7mXrp+dux3i2E7dTE6guGkQNVargu9aoCul/GcAPgjgxWEY/hM6/zyAdwN4E4DPAnh2\nGIaXest1K1L4x5LT6XRJ4VhZMrfBdZBju3hu7NltzcocI47pwrjEQl/+1Tj/+oCByAoeSs7lO9DF\n0jEFnnMlFSgOfOfn5yno+Pd87qVFuvZSFz3HcbbOM+t7d50a5SxBM8ZVf01AV0r5dwD8OIBfl/PP\nAXgvgGcAfAXATwP4dCnlW4dhOO4pm+ObbC6LramugMgAqIF2AKoGPgVdq9N7BkBT01HXUOQwKtxG\n/RmP6wcX06hVdxabXcka6FxMpwbCJVIyb4UBlr3Gr8Z0NRC4Mc2yotlY8vNa8eAYWQl0pZQ3APjv\nMWez/0K+fh+AF4Zh+OTltc8AeAXAuwB8vKd89sm5szRm0FUqLiappYZZeHB4HolBV3NhNR7gclU0\n/a/X6qAq8FjBuT9c1q2H+d10gLKaA0LEdMp2fBygy2LDOGamVSOgRnUM0LK+VOC5MVLdeK3dy58F\n8HeHYfjFUsoCdKWUNwN4GsBn4twwDF8vpXwewNswEnQXFxcpw+m8nYKuBTgGCXdoKBMLl5Upca8o\ny/E5l3WL/lB302UKs77MjEU8xwHO7RUY6l6qYQjQuekGdWfVU4kkWraOk/tI26PgcCxXW16mSRst\n89aZrpTyHwD4UwC+w3z9NIALzJmN5ZXL77qE3TvtsAx8PGDxS3NdvcJLvFTUtdT6ZGyxitRS85q6\nD3GuGwMu4qesTbyP72JfAxofqyfhQKdJn/BCHHM5QPFe5wqzqYAMeA4oDnhuLLPY8Cb0YBToSinf\nBOBFAN83DMNJ6/pVJRrMbAB4t0uTKQE4ZadaB7lBYwXVclxslJVZa5tzh9nlZHeK1zLGdxHzRpud\nZErJe8d06nKq4nNqXgHBn3lsFEBRh2hja4rFxawKMJf40ri2Bbha/70W7uVbAPzrAP5JKSV6aBvA\nd5dS3gvgWwBMADyFZbZ7CsAXex+yvT3/K9ws7nHA48naUMbMDeBOcscxePFcB2IFocZTmfVUt5aB\np+yWDTb3jZatxz3tdQDrZaOMtaOuWkdtu/a3y1Rq+ZmbqaBjkKm+qLHM+swBzAFvjIwF3S8A+JNy\n7qMAvgTgvxqG4XdKKS8DeAeA3wCAUsqTAN6KeRzYJQE6AEudrA1X8KnrUwMei3ZgLe3u2M65dXrs\nlJbPa6LA1U3v4fvCfVYgOPbQurVc3FoZXI+I32rGInPh47lxrMDO3G7H3m7ca/GcjonrJ9a5VcEW\nMgp0wzDcB/BbfK6Uch/AvxyG4UuXp14E8P5SykuYTxm8AOCrAD7R+5zo/KyTowOyuTpmulrAzKKK\nwUCvMVwtkM7ihIwpam6TA170U7if6q66cp3CqqubAa1mnOL+qEs8q/VLAWZENrY1w6TfAw/ZMo4d\ns2UxmY6Fa3PmVt4G0zlZeuIwDB8qpdwD8BHMJ8d/CcA7e+foAM907LK5TtXYaMwEqOs8Pm4NWrBM\nTRzQ+DNfw3WoGQt9pku7KyiyutWSFVoXZSoHPBVXDvcfA1Xd8xrTadnA8t+IZUynMXCtzBrTrcJ2\n1wbdMAx/1pz7AIAPXLfszOprJtOxhSqgm79rKTVwdXI527i+LiZxbmsWJzkD4AY+vgsJ146TLgoG\nx2Bcv1Bux0YKvBr7OyXnLKtuzNpc75ZXUHPHXR3YGGcAju8uLi6WsuSZW/paMN2NS+YKOct1cnJi\nByIDHP8aGcAV68XPizrUFCmSLHGtA5Hu3bG2N+rgAMfTAwoMBl6UGYBzSQiuB4PPAa4GvAxw7hfh\nzsBliY3axoZEx642ZvGdhjF8bxzrfKgmYVx/tmTtQadsp0yXxS8KusjMxWsAomNdoM/7uKa2qRJl\niq1typjRiXOt+Twfa2wUgKsxnXNBHeCUSbI+0RVECjp9PrulPeymCbasj9wKlNgDuGKI1Kg4A3Kn\nQRfirH0GODf/xcDjAQiFzPx0tqKtwBzAkouUMZgDXU0yRVIXU8HGz2MXbgzTOUBrLKvGJ3Mp9ZcH\nCiyX2NC66RgrWLSeNfdSGVLZHXi4EiibUHfj2yNrCTplGbVoEce5bBYPDA8Ou5b8OzxOCji3Kurg\nLLmL6XQAss+rWMiMcZzRiE0NkItBVKlbz8vYjl2xbNUMK7w+09XLja0e630Z8FwihJ/l+tUxpfNu\nxshago7XEergaOasNlBqHafT6VIH8v3sWnGn83MUcDwIUTdlNWe5x0jN4quCaF3DsCg7aPzjlNu5\n26qszgi5hdjaZ8DytJCOY83Vz+qpdcwMZAa4EL7GJVFuAniPHehqjXRuHIPu5ORkCXh6b9zPzBXC\niuxWpygQXJ1cfZ2VztqVATdz/XpjoFo9M/BFP2WxnP6mTvsKgI0zxwCuxs7OvWSwZG1V45qV0Sqr\nJmsJOv7luHZsxnQ1wIXSaRbKXRcSypG5HDoA2fzUGGZziubqqK6VYyC9l+vi7mnVydVPweQSKM71\nrTFVTz1cP+jYuRhc2+uMo7qV2ZTBnWU6Z9Wcu8PiwMZMp7/B07gvynZswm6k2xR4NWZSyQDr3NOs\nbrVyuDy3Zfdm9VN2VSXnTJ+WwUvXMrZvKXQP0zkD2So3Y0l2L7XtY2UtQcdKoArmLGss++KMpiYP\nAnT8I1BOj+vqDaAeXNcsX2sgFEzRzp77MhZ34KkpM4OFv3P3ZcJlOC9gLHCcke0RBVrmHjJIXB+q\ngcgMqPbjWFlr0NXiDO1kBz4X0/GmwXwPm1xcXCwGiANr/l5jJWc82J1R4CkAW0CrsVet/1a5N+sT\nPeZx1L7NDGPNbdTwwdWjFYs5wLnnq+i16l6PZbu1BF1I5oY55okOqS0LU6bjRcKh7K7Tox7hDinD\nRXLFDYIDHEsGuB4AZsBzLl3WdzXQ9IyPY86sjIypXTiQxWt6rABgN7DlWmbPdfVmcOrzxspagk4H\nzHUEZ+cUeK4jGXTxWoFYtbKzs7P0rFqswM+rZbMcW2eA7nUxHeDY8nJsmQGP2+LA0mO5WfEcYGvK\n7Vimh/FqokYkA14NcNmC7+y6+P7OuJcsNeZxbkU2YM695LdaZYykQLi4qC9/GsMUmYvpwJcpLfdH\nSBikrM9i74DX6y5FHR17tliuBjBluGw81SDGcx3geLyyclsAz0A3ZrxD1hJ08QLWlguigxfiLB+n\ngI+Pj5feIHx6eorpdNoEjfPlFXCazVRRlykDRwY+1y+6cJfv1c25bcGMsXcxmfvsxLm8Lr7m9bDu\nV+vuFRFaF2d01QupsVxWllsuxj/O1evuBOh2d3cXxzWr56wlsGyBgv30bVXHx8dLr5rTwckUzsVE\nWQaTgeesdGZZe8CmgA63l6/rBV0AI+rLhsf1h+uL2qZuPoPKvSEse/VeNsYOCAy4Wtay5ZqG18Bh\nSHwfenMnQBdMB+TMkCmgduJkMlliOgZd/BlJvFnZuUcMgEypMtaLe9h15HLV2mrbWNT9jHM6t8jX\njQFdgEOv13bz5+wZer1jOQUe73WqJ0ty9AJOPRhd+O0Yk5cKxj3R1/y982ZaspagY6Zz4pQ3cxfi\nvL4mLlhud3d3sXKix70cy3KqoFlc4trngMd7zrpyXBrtzkCn5WUA69m0fjXQhbK7l8pmrmbNvQTy\nN8TpedeH3P6sDAf8MOBRzzvBdJxN7BF1NUMYeNGZGtfp25LjPi2/5V7WJspVqbWeDlzK4hmYHevE\n964eUS7HnRmTZe0YC0odn9pbo92Pj51ryeOr6z116ZYbTzU4zr08P3/4v+lcRy77dQU6bmhvnMcD\ntL29bV9T7txY7VQFXpYxUzbQeCljHdeumgs4ZssYmp89BnQtAxN15ra4t0lnwMu8mNg4Vnd/sMJh\nhtML4OFaVTenx0Yi6srhynQ6fX2ATpXEdWjGerxiReftYpUKxxPZgLNl5IFgAIa0WC377ECXKfYq\nwGPQcFnA1T/LHAM43Vz2MmM7Bp66dlzHaIO+yl3/YCXGQ5M52nfRZjVGcX2EIi4b/roBXQ148Zmv\nj3101mQysUwXv76uBcgOeAo49zo5Bg/XWevLLiCDjp9dA192nQJFQVO7b2xZrp0KOgaYYzz1XBgc\nqvjKdPpqBdfnUVZck61eiToGq6k7yzFjjzx2oMuUzLmFvFfrCOAKy+nv+GKfAbiWNQumU2C5VL9e\nx20KpmiBLTses429T9nSAY/bxqDLpg1aL7zVfq8xHY8LT924/lTAMdNx0u3i4mLhVsaz7wTTKZBY\nNMZy19TKjQHQADwGjK0bZz/jWZkCKOPprxbUPcraEMet9L07zr7vAVTr+xboxjKdbjXA6fiF4exx\nLVv1zxZGRx0iltvd3cXe3h7Oz88Xq5k0/OiVtQZdBr6aZQnrxOLu5w6PQZvNZovrQxG4DMdUYSXd\npvfV6u3c4x5Q1c7VrlkFjG5apAU87k8FXgbADHA6bu6vuVwSRescfasuKq9AiXFmwAXoYo53lTk6\nYM1Bx5/HAg/Is4M8ADyFEH9FHIDjJEOUwW5KlAH4XytHXNbKOGaurLK6HreAVvvuOuynQHNTJToO\nDngOfC6BpW3mBIpbaaQvQWKw8XSKejo83uFW8v+jB+hms9mClVeRxwJ0Ny0MFLaYx8fHi0zVzs7O\nFcBldXVuSoCO1zOqQjvJYkj33Ng7sLU+OwCPSawo2GrL6BzIsvM6TeDYtsV08V2N6Xic4r6oE/Aw\neRJMt7u7i/39fZydnS3+sjnqOlZf1xJ0nA1q+cy1752F5XvCxYjB446O//1WN4mFv9NJ01giFHvH\nAhmoXRyp0gO6TBwYe+bbHNPxyg31DLhvlOGyWNllK1d1LXliXEHnmE4n5oPlptPpwr2MlxVHzN4y\nok7WHnSAT623wNjKijGjAQ/fpan3ZMqiyrW1tXXlF+tRT3ZXY7Bdoobnj1yba8JuqipBLcZlYLC7\n3Opbp2iZO5xlIrkuzrgoo4Y3MpvNcHR0tNhms9nSdEHG2NG2MLYa96lLube3h4ODAxwcHGB3d3ex\nJjjaHyx5J6YMdGLZDVZ8l4l2YLgDuvEzI/5S0GX38KAGkPSX63FPfB/n+HPUV2M8br9rX+YuZtfz\nntmbFTKMQY9wX2QuYXyXJUhcHeNenYLhhFcGvCyFzwyt7mUAbzKZZyuD1Q4ODnDv3j3s7+9jb29v\nMTnOBurOgc6xTGvgQoLJwmJNp9Mrc0Ns8dglzH5uwn/KoS6Xqx+nw6NsdTnjWs6qxTO4DzLR6zOW\nyxiG7wnlYfeuxmgubnOGwLn4NVFXNhIjPAEeoAsAxvnj42M7FlpfN00QoNvd3cXBwQGeeOIJHBwc\nLEAXc3PRhqgTk0SPrCXoNKZjhghRRlDhVQQRCKvreHZ2huPj48XAciKFQcdJFR1EjhNUqXQOSl9S\nG8kWLVOB4sCSgc2Br2WsePKYLbmTqKcaCHaVHdOxIVLJ4lzuJ85OBuAODw+vAI+X8rFnos/QZIsy\nXbAcg253d3dRPpfBiyp6ZC1Bx5YjOi2YwaWT3WcGXHQagyl+SxfPC8WJAd7Z2VmAUFPJ7F7wIKpy\nux9msvJp3MZla0zCCp0ptx73CgOJj1vX67iw0ciMR8tV1hhMs8sBMHYvdRWKc7U1AcT9GmFETAsE\ny73hDW9YgI3n5VhPZrPZ3WA6ftksdyBPVoe4AZ1MJktzK/v7+zg4OFi4m7EPVyR883heDDR/r/Wo\nKUnURd/D2Yq1ItaouYjcLwz6sXEFi2OmVozo6ucAl7mnWR3iuBdws9nsStYynsEMrHOLcU3MzU4m\nkwXQ3vjGN+LJJ5/EG97whiWDyfODzLh3gukYdJxBzFwmt9qBWU4zUOFuHh0dLSzW0dHRkjIzCFVx\nNOuo9QnLVwOcMxTKBPFsnpCPPYMti716pRa76efadVp3blcm2iauj1NyTaDEZLi+UVr7VucVeXoo\ntgBdAO+JJ55YasudBp3StWYNWRhoodzsWgbT3bt3b+EqsMsQgAuLF8+PYxdfusSJAyJPIXBdnaGI\nc46x+HlRjrqgY+Y2XfmOXR1zOcmu7cmoKmC5Pcx0zHK8BcNxUkTHg0HH14RxjmwlAy5Ax78m4AXy\ndw50zHSc7XOKoaALd4BBF0FxADD2Ozs7ODo6WsR7zHT8F0/8rKiHW0GhgNna2lrEja7Oaiwy4+IY\nUUGn7tkY4LnYMJ7Vcy/XqQZSPucAx2Wqm+/m50LhdRmac8f1VyDhMgbo7t27Z5kunhNu/02AbrXF\nY7ckvW6PO+8YRYGprp+zttlKDa2XU/zWlrUxu46/7+m7VcW5ldk12ufXfZaK9pOOjXOvMyOSndds\nZ/aLh57x7JG1Bt1GXhu5DmA30pbJpoM3spHblQ3TbWQjtywb0G1kI7csG9BtZCO3LBvQbWQjtywb\n0G1kI7csG9BtZCO3LBvQbWQjtywb0G1kI7csG9BtZCO3LBvQbWQjtywb0G1kI7csa/nTHgAopfwV\nAD8B4GkAvw7grw7D8Ks3UO5PAfgpOf3lYRi+bYWy3g7grwN4C4BvBPCuYRj+jlzzPIB3A3gTgM8C\neHYYhpeuW3Yp5W8D+A/ltk8Nw/CDHWX/JIAfAvAtAA4BfA7Ac8Mw/PZ1695T9qp1L6W8B8CzAP7Y\n5anfBPD8MAyfuk6de8u/Tp+zrCXTlVL+IoCfwRwcfxpz0H26lPINN/SIfwrgKcwB/TSAf3fFcp4A\n8GsA/mMAV1aOl1KeA/BeAD8O4DsB3Me8HfX/d+4o+1J+Hsvt+Eud9X47gL8J4K0Avg/AFMDfL6Uc\n3EDdm2Vfo+6/D+A5AP825sboFwF8opTyrdesc1f516j3kqwr0/01AB8ZhuFjwMIC/fsAfhTAh26g\n/NNhGP7guoVcWsCwgu6HYe8D8MIwDJ+8vOYZAK8AeBeAj1+zbACYrdIOtcyllB8B8M8xV7Rfvk7d\nO8teqe7DMPw9OfX+UsqzAL4LwJdWrfOI8leqt8raga6UMsV8gD4Y54ZhuCil/AKAt93QY/7NUsr/\nA+AIwD8G8JPDMPz+DZUNACilvBlzS/iZODcMw9dLKZ/HvB1NJeiQ7y2lvALg/8PcKr9/GIavrVDO\nmzBn068BN173pbJvqu6llC0APwzgHoDP3XR/a/k3VW9gPd3LbwCwjbmFYnkF8069rvzvAH4EwA8A\neA+ANwP4R6WUJ26gbJanMVe2R9WOnwfwDIA/C+A/BfA9AP7XCitaubz+RQC/PAzDb12evpG6J2Vf\nq+6llD9RSvlXAGYAPgzgh4ZhGG6wzln516o3y9ox3aOWYRg+TR//aSnlVwD835hbtb/92tRqvAzD\nwJb7N0sp/weA/wvA9wL4ByOK+jCAbwPwZ26udvWyr1n3LwP4dgB/BMBfAPCxUsp331SFs/KHYfjy\nTfX5OjLdvwBwhnmwyvIUgJdv+mHDMPwhgN8G8M03XPTLACa4vXb8LuZ9192OUsrfAvCDAL53GIZ/\nRl9du+6Vsq/ImLoPw3A6DMPvDMPwxWEY/nPMk2zvu4k6N8q/Vr1Z1g50wzCcAPgCgHfEuUv6fgeW\nfesbkVLKGzDvtKpijJXLAXkZy+14EvOs3qNoxzcB+NfQ2Y5LUPx5AP/eMAy/x99dt+61sm+i7iJb\nAPYeYX9vAdhzX6xa73V1L/8GgI+WUr4A4Fcwz2beA/DR6xZcSvlvAPxdzF3KfwPAfwngBMD/uEJZ\nT2AO2PDp/3gp5dsBfO0yMfMi5hmwlwB8BcALAL4K4BPXKfty+ykA/zPmivbNAP5rzBn701dLu1L2\nhzFPdf85APdLKcEOfzgMw9Hl8Up1b5V92a6V6l5K+SDmcdXvAXgjgL+MeVz1/depc0/516m3ytox\nHbDw+X8CwPMAvgjg3wLwAzeR5gfwTQD+B8x99/8JwB8A+K5hGP7lCmV9x2X9voB5EP8zAP4J5kDG\nMAwfwnzO6iMAPg/gAMA7h2E4vmbZZ5j3yScADAD+WwC/CuC7Lz2FlrwHwJMA/iGA/5e2H44LrlH3\nVtnXqfsfBfBzmI/dL2Ce5f7+YRh+8Zp17in/un2+kM3bwDaykVuWtWS6jWzkLssGdBvZyC3LBnQb\n2cgtywZ0G9nILcsGdBvZyC3LBnQb2cgtywZ0G9nILcsGdBvZyC3LBnQb2cgtywZ0G9nILcsGdBvZ\nyC3L/w90olK5smSDIQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10ea092e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "h = 50\n",
    "w = 37\n",
    "fig = plt.figure(figsize=(7,3))\n",
    "plt.imshow(lfw_people.data[25].reshape((h, w)), \n",
    "           cmap=plt.cm.gray)\n",
    "plt.grid(False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# How should we choose training and testing splits??\n",
    "- cross validation, how many folds?\n",
    "- repeated holdout?\n",
    "- stratified?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Accuracy, precision, recall?\n",
    "- How do you perform precision or recall when there are mutliple classes?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Recognizing faces is a classification procedure that we want to be accurate. **Detecting faces** might be something where we want low false positives (i.e., when we think there is a face, there should truly be one). Whereas we might be okay with more false negatives (i.e., we miss a few faces in the image). But this is **classification, not face detection**. Therefore, for this dataset it probably makes sense to be accurate overall--because we have many classes. However, we have such a class imbalance in this dataset that we also want to be aware of false positives and false negatives. \n",
    "\n",
    "Namely, an argument can be made for looking at false negatives/positives on a **per class basis**. For instance, an individual with lots of examples (i.e., a person with many examples in the dataset), we might prefer low false positives for that person. For a person with relatively few examples in the dataset we might prefer low false negatives (when we see an image of them, we identify them correctly). As such, we might breakup the dataset into different populations\n",
    "- (1) those that occur frequently (prefer low false positives or high precision)\n",
    "- (2) those that occur less frequently (prefer low false negatives or high recall)\n",
    "- (3) the entire dataset (prefer high accuracy)\n",
    "- (4) average accuracy per class (prefer everyone to have high true positive rate)\n",
    "- (5) NOTE: that recall and precision are not always dealt with like this! You should look at the structure of your application to decide how to apply accuracy, precision, recall or whatever validation measure best defines your data.\n",
    "\n",
    "Let's start with a simple classifier and ten fold cross validation, calculating accuracy to start."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KNN accuracy 0.335759179623\n",
      "CPU times: user 41.9 s, sys: 1.59 s, total: 43.5 s\n",
      "Wall time: 1min\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "import numpy as np\n",
    "from sklearn import metrics as mt\n",
    "\n",
    "\n",
    "# create variables we are more familiar with\n",
    "X = lfw_people.data\n",
    "y = lfw_people.target\n",
    "yhat = np.zeros(y.shape) # we will fill this with predictions\n",
    "\n",
    "scl = StandardScaler()\n",
    "X = scl.fit_transform(X)\n",
    "\n",
    "# create cross validation iterator\n",
    "cv = StratifiedKFold(n_splits=10)\n",
    "\n",
    "# get a handle to the classifier object, which defines the type\n",
    "clf = KNeighborsClassifier(n_neighbors=3)\n",
    "\n",
    "# now iterate through and get predictions, saved to the correct row in yhat\n",
    "# NOTE: you can parallelize this using the cross_val_predict method\n",
    "for train, test in cv.split(X,y):\n",
    "    clf.fit(X[train],y[train])\n",
    "    yhat[test] = clf.predict(X[test])\n",
    "\n",
    "total_accuracy = mt.accuracy_score(y, yhat)\n",
    "print ('KNN accuracy', total_accuracy)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Well, that was not highly accurate. Let's get the accuracy up before measuring other performance. One thing that might help is reducing the dimensionality of the faces like we have done before. however, instead of manually coding the PCA step and then classifying, let's train a PipeLine in sklearn.\n",
    "\n",
    "A pipeline allows us to cascade operations. The pipeline can be setup to run PCA, then fit the reduced data with a classifier. The beauty of pipelines comes through when performing different cross validations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KNN, pipeline accuracy 0.372808468409\n",
      "CPU times: user 42.9 s, sys: 5.22 s, total: 48.1 s\n",
      "Wall time: 40.4 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "from sklearn.decomposition import PCA \n",
    "from sklearn.pipeline import Pipeline\n",
    "\n",
    "# setup pipeline to take PCA, then fit a KNN classifier\n",
    "clf_pipe = Pipeline(\n",
    "    [('PCA_Eric',PCA(n_components=100,svd_solver='randomized')),\n",
    "     ('CLF_Eric',KNeighborsClassifier(n_neighbors=1))]\n",
    ")\n",
    "\n",
    "# now iterate through and get predictions, saved to the correct row in yhat\n",
    "for train, test in cv.split(X,y):\n",
    "    clf_pipe.fit(X[train],y[train])\n",
    "    yhat[test] = clf_pipe.predict(X[test])\n",
    "\n",
    "total_accuracy = mt.accuracy_score(y, yhat)\n",
    "print ('KNN, pipeline accuracy', total_accuracy)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is performing about the same overall. Lets take a look at the accurayc per class to get a better idea about how this is performing. To do this, we need to look at the confusion matrix. What classes are getting classified well?\n",
    "\n",
    "Scikit-learn implements confusion matrices as follows: \n",
    "\n",
    "![Confusion Matirx](http://scikit-learn.org/stable/_images/sphx_glr_plot_confusion_matrix_001.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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SZro6icjzgC9l5v3AZsAZ1e+/AZ7Zw9gkSdIMVycR+Rfw+Ih4HPBi4Jyq/NnAzb0KTJIk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\njImIJElqzP8HZoXwE2OFsw4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x136493518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def per_class_accuracy(ytrue,yhat):\n",
    "    conf = mt.confusion_matrix(ytrue,yhat)\n",
    "    norm_conf = conf.astype('float') / conf.sum(axis=1)[:, np.newaxis]\n",
    "    return np.diag(norm_conf)\n",
    "\n",
    "def plot_class_acc(ytrue,yhat, title=''):\n",
    "    acc_list = per_class_accuracy(ytrue,yhat)\n",
    "    plt.bar(range(len(acc_list)), acc_list)\n",
    "    plt.xlabel('Class value (one per face)')\n",
    "    plt.ylabel('Accuracy within class')\n",
    "    plt.title(title+\", Total Acc=%.1f\"%(100*mt.accuracy_score(ytrue,yhat)))\n",
    "    plt.grid()\n",
    "    plt.ylim([0,1])\n",
    "    plt.show()\n",
    "    \n",
    "plot_class_acc(y,yhat,title=\"KNN\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We don't seem to be doing very well... Perhaps we should change the classifier we are using--why would this be the case?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pipeline accuracy 0.340721137942\n"
     ]
    },
    {
     "data": {
      "image/png": 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2B94GXBMR11NqRC6nPJX3qS7GJ0mD1u6pvfY9kMauTjqrXkCpDSEiVqYkJZsB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\nSZJUGxMRSZJUm/8HWO9wri2+axYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x13684e0b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 2min, sys: 9.09 s, total: 2min 9s\n",
      "Wall time: 1min 19s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "clf_pipe = Pipeline(\n",
    "    [('PCA',PCA(n_components=100, svd_solver='randomized')),\n",
    "     ('CLF',RandomForestClassifier(max_depth=50, n_estimators=150, n_jobs=-1))]\n",
    ")\n",
    "\n",
    "# now iterate through and get predictions, saved to the correct row in yhat\n",
    "for train, test in cv.split(X,y):\n",
    "    clf_pipe.fit(X[train],y[train])\n",
    "    yhat[test] = clf_pipe.predict(X[test])\n",
    "    \n",
    "total_accuracy = mt.accuracy_score(y, yhat)\n",
    "print ('Pipeline accuracy', total_accuracy)\n",
    "plot_class_acc(y,yhat,title=\"Random Forest + PCA\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy 0.403241812769\n"
     ]
    },
    {
     "data": {
      "image/png": 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Gy0RE0rgyZ851bLHtMTB5hScL++Yx88x96gtKUsdMRCSNP5NXgCkvqDsKSV0w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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1110d8940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 5min 37s, sys: 13.4 s, total: 5min 51s\n",
      "Wall time: 3min 33s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "clf = RandomForestClassifier(max_depth=50, n_estimators=150, n_jobs=-1, oob_score=True)\n",
    "\n",
    "# now iterate through and get predictions, saved to the correct row in yhat\n",
    "for train, test in cv.split(X,y):\n",
    "    clf.fit(X[train],y[train])\n",
    "    yhat[test] = clf.predict(X[test])\n",
    "    \n",
    "total_accuracy = mt.accuracy_score(y, yhat)\n",
    "print ('Accuracy', total_accuracy)\n",
    "plot_class_acc(y,yhat,title=\"Random Forest, Raw\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
      "            max_depth=50, max_features='auto', max_leaf_nodes=None,\n",
      "            min_impurity_split=1e-07, min_samples_leaf=1,\n",
      "            min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
      "            n_estimators=150, n_jobs=-1, oob_score=True, random_state=None,\n",
      "            verbose=0, warm_start=False)\n"
     ]
    },
    {
     "data": {
      "image/png": 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itAsmwvwC24mJiZbn/KAifgptFIPZ2RnK5SozMzONR/uR2ehncOEv+Sj7ISIy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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114173518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generalization score estimate from training data 0.378920495988\n"
     ]
    }
   ],
   "source": [
    "# now lets get access to the different properties of our RF\n",
    "\n",
    "print (clf)\n",
    "\n",
    "plt.barh(range(len(clf.feature_importances_)), clf.feature_importances_)\n",
    "plt.show()\n",
    "\n",
    "print ('Generalization score estimate from training data', clf.oob_score_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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31AgnhuLqmgCF7WGE1kPvkQJr2Zws1bauqbtrQwKg1DcEfcdQmPfEThwr0MlR\n9k23X9MSWFzfO304/bMdidWkIJPELb0fY+9VJwooylASoNze3q5ymBbtaLdCMDlzC411LatQZkEH\nSw7j8ploPCO0A8BJR87glwDLlUlne/Xq1cHu0TWRbwK9pXoRrF1kdys/E4tQhqLl6EoL26ZDsZS3\n1k9lAhYH6Po9r50e09HsRKcR3F/4TnKSgPLy5cv9df+mgO+N7e3AS0Ci4hhKQuClDnFCA1hTp8n4\n2S4XqVwedD79TMBJ4pxayyHlTnksgUFiLxMjdI7Wcy3KALkXw20v0InNto+U3jm56soxpAQiSzpw\nssRE3PcpKNIXdD6y50/6ehriU04SUJzBtEysJDm76/AUJSaqrbSXadawDLbJdT4NPtWL5bI97Vjc\nM5N0pgyD+nFlOwdxju/YCX/vkpxV9ZsA0JWpmxG5U1TnUHa73X7/xfn5+Z6tclepMgPmzcOBjLZR\nbcg5+5IdMahQf1oO66Pg0ff6fu/rcsO4T3WVZ7PZfG9V/btV9d1V9Uer6ge32+1fRJqfqKofqqpv\nr6pfr6ovbrfbr6wtw0UBSgKWyXH02T4nA3CGquW5OibjX2JB6mx6PbUrRTO9x5UJ1t+xnwS++pwC\nCI1Y81NAW8tMKFOA0HqrHlXcLlaW2eBLsNAz81eAYb9Rf1pX128MaGtAhfk5QHF2SebUINKA0ve6\n3gyiS/IuDOUPVdVfq6r/vKr+S3652Wy+VFU/UlVfqKrfqaqfrKpf2Ww237Xdblf9yugYhpIOpk1l\nOMfVZ9h57jteqzgg03JdVFFASW2ZnHBN2zSfxFo4/tehhGNEqiddFdH7ru5rdZzAJTm8Oj2BpaoO\ndrS6/F39FGgcYLTQcfs7ZUdJHKgwAGgbXUBKDEX1q4BPIF4KXEmOBpTtdvvLVfXLVVWbzcaV9KNV\n9eXtdvtLH6f5QlV9rap+sKp+YU0ZU/SvKmvsa0GGRtfn5Ewta5A6Gf8SqDnqrBEjMQVXrt6jgU/A\nq6Lt7zx05UKdsstSEFRnm3S21M+ubtN3/WxvdHN6JWOa8qZNpGeWgprWTx3cnSdJ/eyGkg5Qdru3\n/8nMxYEqv4X/mOFO1Sc8h7LZbL6zqj5XVb/a97bb7dc3m81vVNXnayWg0FiXGMq03EXn6Lwd9dX0\nfMZF5zVRZI2oAWg0ZZ00ykx5pbNjHK4dqmuNtO4vR2iAzkGme06Hrh78TsGOwK8yzd+46F51+FMB\n2oI70spcPhMTAAAgAElEQVQh2RPbeQyYJB05VqKA73zH5dO6ov196gxlQT5XVbt6w0hUvvbxd6vE\nRUwXMdYACw2i6qHjHxN11FCcwThQmTqShq2d2nk1iChlpZ6cwWv+mkfnSeB2Okisi212kSyxNDJG\nSteT5Sw5KNvsom1yQP0+7bpODKBBpJdYnX2xbgrW1NdSwNB0ro1p/sQBul43s9O/H/2DBpRPRLhs\n3MvF/PvElmYN7CildXQ4dd6W/q4jMWlzipDpzHynqOoYU2qf1oHijLfzaBAhMLn6T2CT3qDnhjk0\nfpcfy0lBYtJ5qqub7OaZ1w4gnfPyPofFidX0dxwarmlfYn4T61wCKFdG2wj/dGxJPmlA+WpVnVXV\nZ+uQpXy2qn5zbSYvXrzYX+vfjOqauDopjxbtUJUUtfTgkIfGsBQhVRQQ+DyjpcvHgVCiyQlQCCbd\nRtd2dSjXzvQGPa4SkAk5fSdZs2Vdz44VUr9rwKT7i8I+c3pxOkv9kVh3koldEExYB1enqZ6db9rR\nPcknCijb7fa3N5vNV6vq+6rqt6qqNpvNH66q76mqn1mbjzKUftmL/qKYDq0GRcd1NF0NKQGKRnQX\nXShrIrCm1fPkZC5SpjowbwJKApCkA0rrNjEUjWgEAjfp7Ord5aSDaakPlzYNVaY2Uy9LQzQCygQq\nqZ16j9euL8jK22adTvWzA7kEdp86Q9lsNn+oqv7BesNEqqr+gc1m88eq6m9vt9u/VVU/XVU/ttls\nvlJvlo2/XFW/W1W/uLYMZSj9Bu405EkORoSlAmngmpZgouVo/i0OINzuQucInd51bLo3SYqiyYGc\nw6lzuPY6MOE7fpOe+MrBJG64k9qZ0rtdr2nSkvrS4YizMdZHWahOnLu+U8BVnfHQCfFJTy06lHVB\nQe1TP/Nayz8GTKrejaH841X139ebydddVf25j+//har6M9vt9qc2m81nqurn6s3Gtl+rqh9Yuwel\n6hBQ+oUvPeRxY3KKRkWH2A5UuiPaMLhsvDTccA7M79N5ipJ08jWA4sBHVyAcO0mrBE749ryexKMR\nqlNrGc+ePavLy8vF/ktDWbatnZirM/2da5vTEa/V4bVerCOBwuWZAiDz0zZ0v7lgucReUgAiqPQ9\n2t67gEnVu+1D+R+ralyc3m63P15VP350bT4WHfIooChDSUOBqtqP55ecVFdUHEWfDJu0eglQ9F5i\nHc4IjnH0yUkUSJTGunehunISQ2lgUVaXXszcDKWfW2Io7kg65rKtpnGgMpWdnJHMQe9Pup/y41m3\nv6fyna7W1EP14fom1esYOclVnvv7+/01EbuNQ18CTEcnqk90v78nAyGAsKPU+SZwUJnqMR3THAs/\np/kBfUv8s2fP9mcHWASYy8vL/bJoVdkJWVe/1l8DSL9s/PLy8oChOMdy/eqk81A70Te4JZbnGEgq\nJwURJ0yr5Sw9o20ig+CyvwM26n4JNAm2U53WykkCCv/bOAGK3neUkc84x5yiEDuFqxUJVDqtA6UJ\nKLjyoM/wuoXRJzEaBZF+pYDOMfDZBoLLy8sHfZDKSLp0YNKAsmZ4s8a4E6N0ulMgS2CyBmwmxuGe\nnxyc4mxCQeVdhiRTwFJAIRAeI48GUKoOwaQ/6y8jE5j02Tmxi6gTEDjnS+yBQsBgh9K5nSTa33m5\nN7C3ETaVblBx7ypxEZziQJn1UlBWhqLAohGXcx/HyBIAJdah5SUwWcuQHMOlLO0RIeA1SPa91pdr\nh5OJpTh71mfeFVROElB0yOOcm2iqB6NpP5NorwMCzWuN4+vZiRqKe45zFxoxUuSm8TEvHZb0vIYD\nEgcmDvhc+5Lj8eCcSwNL1eFydhoiELhYPnWi14kBaf8mUHGAkpht0oPTF/NxefG+033qjyWZGAqH\naseyoJMEFGUo7FA1cEa0pSGPA5MWGiBppXN+Rx8p6gTJiTtPvofD/XzALZtrRNNJT12BcexEJ2od\n0E3/Lsd6KVOkDgkkl5eXdXV1tU+j8zv9vNNrAhRNz35hXfWo8n+BkQ4HcFqG1ssFARfknL24clR2\nu7eLBnqv20Hwc3V2AZJ5vJcMZaKEdPo27L7mcwkElqJZ53F2dnbgWM7gk7AD9bP7A2t94U2XoTP/\nzkAUCHTegiDihmvUTZpIraoDANA3fRFIOl2aQ+n+6rZRX2xXS2IOTr9aFwKX5jcxjslJXTBRPWgd\nHaC49k2BygGcfjcJ06/xg2PlJAElzR8koTOxAxNDWKPgNSA01Yv5qpB6J5q9NOyhMCJXlY3QmheX\n2dURm+Hod+4fESddajn9fAMK/7SNzzcwaPTkQf0phefkPfPvANHlTAzFHVziZZ3YN6nP9JqByum5\n9TEFMs07sQ4FNLKU94KhcFv2REfVKJxD6rWbRKXTq2Fq1HORwl0n4NGzo5SshwMAN0Gt5VYdOqy2\nW6OznpOTkGno8rA6Kfd/qOFr+xWclIG6emn7WhcOIPSsc1B6TdakIKg6TIEosQsVx7C6zc52XT6u\nHBW2Q/M4FlQSSDK/pTo5OUlA0f9SVUW6jq56OxuuY3xNpxGDk55VfiWo6u0P6iYw4Wd1BP3M9nT+\nDnDaQB0AdDTsaNplaTsVTHTi0f19awIUBYlnz54d/FcL29HlKKCoPrQP3ftMCUrdp10/vU4gq3M+\nusdG6+ec2gWY1L8JcBL7dGkn53QAlsBtAhGCAfNyfUQb1fMxcpKAogxFjYHGqZ2p0YxptAPSvhGC\nSd9bYig8q1HqWL7TsFP5HIcW7uh6kE11vl2W1jsNeRKgdF3cMNFF6AYyDqcUgAh0XV83DCOYKMhz\nDqcZCuefpnkX7Vv3AmsntCnqUNO5spcAg2mn4aS71r7QPFtvE6Co3WqfHSuPAlD6rA2kQvS1fxqZ\ntXMUeNKQp2r5dw4uWpAFtehnjbLtYM6pp+GOsopkpDrRqUCjRroGUNh+gjfTOoaiOlMQYd/ouctV\nMNG26f/GJECZfoBIsNddv2SXenbDOwV42gL7yPUXAUCB1YFz6iOVzpMB1TH3JTs/Vk4SUHTIo8qt\nejiDnujmWsdRIUq751I5NCLNT4cl+twxw46Ufiniqc5chF0CFEqK7FpfMq/ux06THFCfUzBRtuH+\niKqHOPxJgLZP66LnbkevPE0reLqqpQyJ/ZCcl4cbxrshoAZD1be2ZUlSuaoHZ9/HykkCisrkxCmK\nqvH2M5ofo6x+5+jl1GkOlNTh2rgnB3WR9eLiwi5vuv0iamRTdHTzCIyWSwba9eXzyryo707nyuV1\nEu3vzrfbr3pJDIpH6/jq6qqurq72e2M0mFFU19xlrG0lo0lg3mlcXzAochWL+nA6nPqSdtHt4XNr\nAavlJAGlld3XHGOrkgkqHLuqobu0CkI0eOcY+pkAtNvN28wbZHrVRPdpuN2kpPYaEd2qhgOR1qGC\nDFmftu9Yw3TPUM8pOi85vR7drz0saQbSh9OHYxutJwWT6+vr/XXnm1iuOl3bIMFdJ4fTsJU26oR1\n1mfcsGUpD5bXQMI/R09D2jVykoDCnbJpQrEPdmg/5xDcRaqWNnJ2louMvN+flT7zh3BuMnEClPv7\n+7q4uNj/JWR3voJTYijUj7ZN2ZvqZgLRYym26liBjUM3TpI7EFCd63yKo+2OwdFRGpCur68PjgYU\nztNUvZ2X0jxaX+2Qyi4ZCLTfad+JfbHttGcdPjpxzFwDx+vXr/f21Lp1xzFDn5MElImh0CBb1Mn6\nuQldCUBVfh5CnVCfZRTug7tB+2iazPFx1cP3i/TRQNKG2oZb5Tt/DRtQ3bKtjk6/K/11Bqz0uik2\n2YMu9zo9O50n59N8GXQuLi7q+fPndX19fXB+9uzZ/r+zte66+qZ6677oeRz+qluBicNsgkJiHE7v\nk5MriOvzqlfti85PQVGHtY+eoRBQHHV00VQRtaNyi2ModETnAImSuqi3BCj6ng6lvglQ7u7uHkx8\nTtGE9W49tS7oFKprBRYFII6r19BgNWg6jzoZX+bE4Zvmxz527CzVxQHv1dVVPX/+/MHx7Nmzur29\nPdCnbrZzelDmwSHt3d1dnZ+fHzAB1UeDUar7EpgsOb3Wl2m173vYmPxqrZwkoLghD/c4tAKqHoIE\nwaHzcYapgEAAckMHB0hq4OmHcF0vDtd03sW9Ca0NMgEKmRbnmBgFneG5zWbU5TTWT1GV+lea3b/9\naZ0pQ+FOac3TvX6Sk8Qcrrp+ury83IPIBx98UB988MEeUAgmadjU1/q7Kx3e6FBIQU/7J4HGks6T\n3pPeeNbApuza6Y6sdpKTBBQVbSAjr1u16HTpvGZ+xEUiGhXpYX9WEHGAwvZUlXWQnnxMY1pXp6qH\nE7BLRql6cQAw5cHvkp7Zb+pQKgRHSjtv61l/dJjKdGByfn6+n4xtUNEhTwNCgzmd2wUkPdwwRoe5\njjHR6Z1eqfuUnjrTM/uu6nBEoIHkvRnyuH0obkjCMWBHFI5ddWlOO1YNRYcgND4FDB2D6yScGro7\n0m9F1EncpiyCVztPCzu726K66/scwrj5FA55XAQlKHZ6NzzVqN3lNgPR99GSYWnbWBfnfJpO66l9\np9e6XMx34yqgqZ0lsGq70/ZzZa6PNKTgcFhtddIHJQ0V3dCHZwZJff3FWnkUgNLH1NGaRo2ZndKd\nr/k7w+w6JFBJwxQ3h9LGqmWo0NjdHIFOzDrW1m1R/dC4FEzaITS97jbudA5M9KxDqyUnUgDtNnF3\nKifTHTNkYNE2OmZJ0G/gd4CizqoA6wCF3ztA0aDm5v8IKNQz+9KBq7OplFbzpA055v3eAoqbZVck\nPzt7+CdTHBp12pZWIhWu9xJTce/44D2+k8QJ6TMPbnTrf09k+wiMKZq7iTcXdfUZTeccuXWqTsRl\nbj107sHNL7iI7MCE9WL+BBM9OwapgKJ5Ewg4bGMAS8DKyXiyRpW1CwpM4wCWNq5tUJ2r3fEFXWvl\n5AFFl+Oc4aVxXkJ7dUJOsCWG4O5xnoSMRMFEAYWswTlBn9sQdW9EioRu+KeG63REPerwJT2XomE/\n22Byf3+//y8lDjucPhkoFICTY2j9CSLOMQgojqFUla1Pz304MFF2tgZQHENhf7CN2gecf0mMlezO\nAYr2J/vovWEo/WawqtozD+3kKk/Z+noaO+pn3ayk2+NJxx2ocMu27rRMKzZap0RH9XNPEPJIv2fp\n9rehE0BcOeog7STtOAmA0j1lKPqf1Lp03u3SoY4DCEZlAq4rX4MDJ7oJ8C4ANHtjXXTIc3Z2dgAm\nfV4CFA0CZBHaNgWrJX3oMJrg4PImACnLdUMe2u4aOUlAYQN0tt11Nmlpivj9HEUNvT+r0Tu2olFO\nt283fU4rNhOQ8OyGNrvdbs8Azs8P9zeoMVJfKcpzArWfdUuFrVs3vlfHa0DRDWIK2s3wHGDpxLhz\nENbHOZz2FQEkDVW7Xx07cLal4JKGO+6zDnm0X7osHdo7/XDIo3airNSldYCienRzKNMyvpOTBBSd\nNNWOaGklMVJUHY6nXQTsNHpNxaoDd3kq3fHtLDSI3uVKQFkDJtPwpEU7PkUnd9/pIR1rRMEkjd3T\n8IEbvVz/dFoCulu5UcfnXE7/jEGv+/+GdHigLFLz1/fpOh2vGe7wdQtsM3WqTE7rx8n6zodMU+un\nQqBWppr6yQWXJCcJKO4VgW68V1UPQKWpfgITFQcsCVT4vU426vfdCQoqfNuZYw59rWd1SEYZdTDm\n49qhciyoML0aIIdGbujW99ThFVCcPtSx2F63XN/zLg1APXfT80/6U4a+1+U1e2KZZDgcIqgDE0z4\nWx4dnnIlS88EOGVFZMkKDgTU3W73gFmQXbJsZTDaT49+DoUMhWNPNbKqhzPcjgmos6liJ4dSkGrp\n75wDa0RVMGkD6LqtPdNp6HRrwET1Rja21P7OW69V1jIUTa+Tx2lYlgIAJ1kbKC4uLh6wGR2aNiPR\naw08nRfZrQ6b+hllItqeCUg416WA4uyzddFDQ119csMXDXpuWOV0OwWyzqcD+6MHlOm/javykEeH\nPqnDqvLEoos6nVen4YQlwUSHOvrDPu0UOozr2LOzs70Rk/YqmLKtWp92NL2n7Z/AJDGQfo4ArfmS\nWej3nDye0rr6cO5DmaE6m07OKlvsIU/bioIFnZlLzG2Xnc4xEwcmBBWyaAes1EUKkgx2CnrJDxyA\np35y9jrJSQKKNkad29FgggmjKZ3NyeRg3flLYOLoNpdJJ3ER4/r6em8YSn/ZNk4SKij2c50PKfua\nIU+XNQGJS5+ArutBh9I8td8V3Ps9MWoTCk7qnA0KupLTwLLbvf3P5aurqz3j6LpztaPnUNjuNHdC\nMNFDJ86179Sp1zJassQJUGi/DtQ6TwaftXKSgMK/Iu3OZoc7+t4A0Ol4poISmLTB0imcg5Bqp30X\nWjYZV59dXXWMz/b32RmWo78OCFS3/OwAxLVBRQ04sY5ul6bT9rhhRFWeU0uRWIefelTVHkx0syDr\n7uZQlGUlMHHA0vtyGlA42aw6of7IAlsSQ9E5ImUzCVRSP2lwXyMnCSjaiFYSjUcV04aoQ54k6oA0\nIMc8HNPpfDTSNKg09aaxLNHOvtazRmIXlbWeu92bieKbm5u6vb2tm5ub/XF3d3fAXPTsNl2p7gkk\nqa76vfaPW3J0+Wl5rm1q4Dqp6+pDQHGrP/0d5yKurq7q5cuXdXNzsz+7t+YRsAnCBIVmilWHb/R3\nAcrZKIOn6qzL1MDjGDL7yul86p81cpKA4iZB2Vmq9E6XJmn5PJ2GxtX3OYeiZ9LWBhU3jqdh6Xcq\najxdhltF4Hdd17u7uwNn6Ovb29sDfeg1lzOpb/aH9gHBkf3jJDEz19+OAWkUdsyRh+quJ1+5a7kn\nIHsTnu6h6c15nAshuLiApyBC22G7ux4EE+reAX7Vw2Xv9gdni3ze9a+7XiMnCSikWYwAzQ7WIK4z\nUBq7cwIqudPpNQGDUYfXumLTtFuNzrUjzSXo2L3PDSgvX76sFy9e7M+3t7fR4dwvsiddtv7YZupJ\n5wAcsCsIHCPNUvQ5HZpyoleHpH0mW2jd3dzc7Odo0pyI21OSGG8zBTIW1TV1Qid3gVPPCk46YZ9A\nVvXo8um82J9r5SQBhQxFzzrkSA3nxOTENNy9LmsaPyqo0YjdkOb8/PzgT8JTJCczcCsJVWUjZTOT\nFy9e1EcffVQvXryoFy9e1M3NTWRIbjjV9eAQyU1Qd/0VQDgcdUdiJ1o2gUzrpPNqzQwdqBBMlE0q\nM7m5uakXL17sgd7VgQyFLFHtUNvXANj61yVnxzRbn2QPThdqj25eMdmW2hjr69KtlZMElKWJoMlx\nq+rA+FvcrLcejkq6IYLml/Jy0mN5UtSp89z8SRujW0lQQHnx4kV9+OGH9dFHH9XNzc0DdtTnNA/Q\nbXbsSIGJoKE6TuDEctKQR0Xz6np0nRyY6NkNRRsUlJn0apBjl1UVhz0OTBpslSl13RtUdHK320Mg\nadBX3fDaBbQ1enfXxzJGyskDCgHDfXZRibTY0fU0EVs1/1+Klu/qxmeqDoc27ZBqKCpqYGQpfa2/\nl9Eo24Dy4Ycf7o92mLSHY2IDygi6X3SileCRmEcDMCOxkynKKkui7h2YpHs9P8LNgwRcPTswcUMF\nOrWChOpCdaM6J7BwXkXTapmcgE3BiLpN4P4ucpKAcszKgGMFE1Pg8/05OUSi4YmRJAfVSVyl3nx2\naocyCZ0o7OueP3n58mXd3t7uJxTv7u72+XG4M9V5zdDD0WU9p3yWQEOdj3pnX6mOtWwOSQkqum+o\n+0W32+uO3GYVbrMa7cqxhNR+199uQp8A33m7oMZAqWndff2eeR4rJwko/CGWM/AWNRq9N1Fnp0wV\ndiLZiivTKZ91aIPUVRfSXhWykaurq7q9va3dbvfAqDvfXtW5u7t7EEFdOyfwSIZP43YArdfaH46h\nuD5ycxQTK3H10/IbQPXc1w3wbgK8mYgCCueuJid0AOoYg85FucCjOmnbWHJ4Mo80FGWQ1HYcCyqP\nAlBSxGIaVdgUBRnRHPvpsy5PsmxGyr6mw/W1e9WAbuKjtAH3Uubt7e1+UtdtoupVntvb2werNxMj\nSKC79IwD3AlYCfSaL6+dc+92uweRWx2OhwL/EqjotTp8MxSdwNaVNc73LTFWbR8dWVmSAkoPj3s4\nn/pNy3OSGIoLku8VoFxdXe2v1XncvIaTBDzpGe1UN+Rxxq4rGS0EE+0s5qkG6+Ts7Oxgb4RScI1W\nem6WQoayRmfa1mPBRNuW8nVgkoCeTEYnhFU/6oQOgHTexoGJ9iUBxa0OpTmtJX1SZ07akbkhrQ83\nJ+jAK8nEULo9tOk0rJ/kJAFFGQonsIiwLQoKS8arCnUgohO8it76/JKBdDp1Bh3etEHq/EZLX7cx\n9eRhH60XRm++2Oj+/n5VPamTY9q2BCQuzwmwHPNJoKgRvXVC/Xa6BCrKdPpawUSHH44pa5lT23mt\nwzXHUNIvzTuPrmfn5cpu0bkRAoQLDPr8ewsodE4CRJoEoyQg4qsA3AReyn8pijsg0ajY4oAtTeQ6\n0HTDoDXMgHVO7Ug67OiZ0kyA4tKyfX1N0KWzu+EkGUqapOWEqO4dmYZVzg7cmfd61U83o3Vb+PpK\n/XGpAvk0vKRe3RCGDEX12tfvBaBw9WNqWGIebmKWwNN5Mx8t19WBhxqmdrZ2YqqbdiSpphoPX9KU\nDFsnMzsvjcApQjlmoDpa0n/qB5bhPrvnloCHdXHt6rNGaFce51q6XO0DdWhXn6l9PKe6u2XuZFd9\nPwWASY9Mq3am7Z2CT5KTBBS+D4VzAU6ZylyWoohDa32WeWmZ+qtQMgeuOLDjJ1Byh+bj6ukOAlzX\n3/0lghs2JQPUyJVEdZUceQKFYyQBYOuOr3twrKTrsCZQKGtk3V09prkVMh8FD5dnsn/6wATCBJnE\nXAkw7wVD4esL1OCr3iqUSuFY210TTFRhmk4Ru9ORnrprTa+02QGZGqmLSlNbkmE4UOq6u1/Xcl6A\nw8rEsrR/WqgvBRVNQ3F9kxySfeX6qXXXu14dOFA/agtrDs3H6ZBg6spgf6s+3BBuCqq0Xdc/mpez\noQQi7yWg0Cj7UKVwNYCGT8U45aXxqYICX8nH+vAz699la94OpKYJwBQRtV4uKjtA4XWqqxq402+n\nIUCoMO+Urts/iQsk/eySLtgm1of9mlhF24WuttEZ3aHf8Zp9SuahAOSYxhq9TcxUwey9ZChJoTRo\nndlX0Y6alENHmSKXu6/5aB3JMChp+KQG2mdnZM6huJfBOVBatXDgTT0mlsJ07v5acaCSnI7fa7sd\noLIuKWA5QOFekZ5z0L7qPNMEcCpb7+lk9BKo6DO6YS71O/3KBSbqdI2cPKBU5XG3o4RcSiNrIA3V\n+1pWn90KQIpwmg8ntdJ1p3VvfGsD7eXfCUwYVc7Pzw9WCVh2yoPUV+tIfbv+UVnjOEvGOjEV1sOB\nexqeTn0+BQ23pOv0pXbgmI0LNK4+iUHpWUGHTD0FvCUwoV7XykkCSvq1caKpqhA3fuXzKa/Ehvqs\nEWrKm9J5uaXQ3e7h27WmvQfOufp7Bb42+t4Up4CbgIQMhTqio7podqwsPZucSvvIUXTVhfZdAhTH\npggs/K2P/mI7BRmCSfdr0r22T8+aL+vGPu3hl2NZra80Ce9Y3zFykoCiMlFrOr7SwL6nZ02nRtZ5\nKQPQ/Kl05qOGy+v+vNvtDn53o3tFXBRTA2SZHRX5e5ezs7Pxb1AVdKg/6kz1kEC6decYkF5PbMCd\nnb6dOLBx9qJssYcn1KnrVw5TONTR/SE6Yf/69dt/KphsgsuzS4xF28UgpfV37UtgNOlW+2+tHAUo\nm83m36+qP1VV/1BVvaiqv1JVX9put/8n0v1EVf1QVX17Vf16VX1xu91+5ZiyKDQUonvV4dzKkhII\nCFwtWGPUdPw0dGmH623x/Qa1LsdFkjZgrS/nVvS9GhOgKGhSb6mNWj89JwClztwEb3IMAvmSaP8S\nsJiG0Vbn3sg+GWjScMV97jp0X7shA8Hagcnao/Pg6o/20cQwJ91Sv8fIsQzle6vqP62q/+XjZ/+j\nqvpvN5vNd2232xdVVZvN5ktV9SNV9YWq+p2q+smq+pWP09weWV5V5WXTJUV2eoqLQozernwXCTie\nTn+Wvtvt9q8W0JUBx3SUOXVZHc309yXKbjovMhN95SGHTC4KuoivabRupPFq4Errnf6r3lLvvl7j\nQAQSvXagkpxW7cCxQhcUnI1NtraGhfGz2rVb3dP01BnbRH1Pwv5XYDkGVI4ClO12+yf182az+dNV\n9f9U1XdX1V/++PaPVtWXt9vtL32c5gtV9bWq+sGq+oVjyqOk9fOqQ0XSABPo0DkcC0pRloCiLy/q\nv2fQ66bBXZ/+HU9vD+f4ntfa9jRUqqoH4/tmKOro3Y40Vu46Ljl1syiWoUCpy6ik3TqJvhQVl4w6\nfZ/aqABBECEY6++FUlmOYXb5DiQmkCEodz4K0m7ZV+vCuk11T+yO12vkW51D+faq2lXV366q2mw2\n31lVn6uqX+0E2+3265vN5jeq6vP1joCS0Fu/q3rbqVRwp2shqPT3S9FZhYDSwHF1dVXX19f7oz/r\nkKrBRPezuGjJju1DXwrEVSc3f9IMhmmTEbqhX+tdgZcOqG/KI1AwqrOvCHCuD9lPydg1n2lYof3I\nVTGnR8cMmjm6oyr/FQr139cKxq7N/ZkT7EyXQOQYcPjUGYrKZrM5q6qfrqq/vN1u//ePb3+u3gDM\n15D8ax9/986iiLxEpVUcEPRZHYwrFimK9JmA0mDy/Pnzg+ODDz6o58+fH7yYuF/XqBN57pw6v41Y\nf9/TR5pD6clg1z435FNnp951wpFMqMGEDueYF9O5YLAGVKY+d/tUXBDikFVfmcn/T+YWhQYjLikr\nm+y+cu9QYbvcqwoYQI/1A6e76Xue18q3wlB+tqr+4ar6p7+FPKxwH4qjd8cqskVBgbPszMtRdc1H\n2fbpyPEAACAASURBVJIazt3d3YHjdJp+8ZFOoF5eXh4wjW6vq28L3ximk7L6y191VH0dZNch7TXQ\nOjtm19+1k6Q66dBMh6M6b+UYAw1ZrzkxqvlqfZVRuX50k6ocDvJX4mn40rpQO9A68d01BAdtP1fu\nXLlkj7QP91llyY80j98XhrLZbP6zqvqTVfW92+329+Srr1bVWVV9tg5Zymer6jfX5s+3UjmquaSI\nNd875Kdhk673d/1MGwqHIFpWR+IGlO6oZ8/e/O+uM2Z1DtbNUWidH2Hdzs7ODsBEQWiSRKU179vb\n2z0wu5c+6WSytqOXvlM/peioYKA7i9tmlPXoJscGPg4ryfBa1C408FAv2j/Kct1wjjbsrtM9N2mt\nZbvrrovr18lHEpivkaMB5WMw+Req6o9vt9u/qd9tt9vf3mw2X62q76uq3/o4/R+uqu+pqp9ZW4bb\nKeuUqeKQdFIYIzgnePtMIOGQoR2nhyAOTBKg9FCJbVta+Zgm+jSN1klfZq2vh1zSFfXRwqjcOnSH\nrjLpZKjrz2moV/XwZwp8V4gCXvctl4oTmCgQuM1hThdqd86hHYPQ+qWhPPNy6ZYOFwRd3lPfHyvH\n7kP52ar6V6rqn6+qDzebzWc//urvbLfblx9f/3RV/dhms/lKvVk2/nJV/W5V/eLacjjGnDqnagYT\nd59gkgy7z8nItaN13KsRstlLVR0MN5ShJJq7BjicQdARtC7pxUvUmXOk5HDUR6ozl15J4dmfTudk\nKFwWb9vRIYvbHOiGO+w/2lKaC0oAQTvSs/bDxBh1GJwYetJ3suGl4Kw2oO1fI8cylB+uN5Ou/wPu\n/2tV9fNVVdvt9qc2m81nqurn6s0q0K9V1Q8cswfFTVq9C4pOz5ANOABKQx4+78BEh0K63brZSjOU\nzruZS9Vh9CewTMOE6b7mpQCWAMpFcWUCS4dL1zrlPA+/pzGr7gkmPQGq/aCA7tgn2+aGqQ4YdYil\n19rXCtqtR87ZVB3OOelzzuYIWknntBfHvtq+1jAT1dlaOXYfyvyGnbfpfryqfvyYvFU45HHj6CQJ\nVR06axSZGIreI+0nIHSH6ipM+u0P6bquBGnUciDg2MOSk6fx+8R+nC5YJ42uLrI7kJ6MOoFJf+cY\niuqy66iTowlQEkNhH7x69epgJUfr0wGAf77WrIwb5aoe/gthX6dJ56VhDvuEgEJGlZjOMeDh5CR/\ny5OMjA7E7/V5jm2ZP+l63+czruPaiPTMPBpk3JLw0vKwOri2Tx0m6WwNSNBwyAhcftpONxxzz7WQ\nHbloyfa6/NLwgixEdZZsxrWr07nhobJm7Xdu6FPWoWXopHTVIaA0ELnNitxHs8QunC1Vvd3fwrY7\nUFF9HwswJwkoOpZ0RkGKSllj4IrWCj5Mmwyv68OzGz9XlY2oWgdX937eGZZrZzIMx7y0vu0UXZa2\ntdvOPN0wwgG3Xqvjsb0JwNl/jjnudruDyKw6S/ai7ap6+DMAx8DU/lpviSU020x9wUlyZSgcWk3M\nRPuS9dO2sU+czpW90C7XykkCijNMNY5W+BTR3LWiLmlukjYkGiAdU8+us3rzU+89qXr4jhGnA8dM\n1KGniOUiNoXLoe0o1MHEYlJU1M+dN3fQTiDonEeXgvU5AgrZIIc01CFZkzuUjaQho4IK26O/yiY7\n6eueD9LJZv21uObnRNvqdKg2TEm+c4w8CkBpSYBCB3ZzI87IE6DQWF0nOCo9GeT5+fn+X/8Y5bSs\nFHVoKDpvoaBH/SmQMl9tn9YnRbRJEjCzj9xcSxp60lFd3n04QNFIn8pw9SY49Gfd8+LmNdjnDiC7\nv/VP7vXM4ZHO1yS9dnvVDrV+rk6OXTPP94ahqPGkDUnTW8iYR0uKrHQ2RuMJ3dXRu8PSblEFk16h\nSMxCAY9j6gYQjfaprfrZDR8dmJDKU7dLLM+VrUCirEDz075wUZ99xfyOZShVhxvYWC6v07wGnVaH\nPArYfVTVAzBRQHE6Z9unQKhBQevg9tYk3aRAvCQnCSiJejlndB1AwDimg9zcSjJm1yk6Y8+xcdXb\nuZRmK1o/igKKzr1wjoOsQnWXGIpG7cRQEktQxyCgqN7YhzqByjG/yydFVz27+mobp4lvBxrTve6H\nadjjWIoy5a4rV4XcPz2qDpO+tU/TZwWUnpzlKldq/zREcnKSgKLDACIoFaXCTkjXei9R9WMkRXMa\nlpvo4/dro0Iqc21kWfM99UcAZPTn0GtNOUyvxj3Vaw0Ddc/1sxos6ERMr/eUhfTWAPeTBseqWmfO\nNrQOjoUmZuHa32Um23b5aL20zuyfJTlJQNFxI8fA3WiH5qTIzjDd2JHfuWs39HB0kel7aKaRWNvQ\nRqMUufOg82jb+BpJ7lPR+rTofQ7TXFTutM5Q04YtBWl1XqdPZ/Cuv9TgJ9aRhACvG+DYRr3vnKqf\n7+da9woq7ANXVrKVDqaqO+ps0h/1qH2kv25n/+mQnOdj5CQBpX8eX/WQnjvDdw6xBCprOsWhuAMV\n94x2UhsK29CGqbtiHQgoUDYIcIeli3bMp8/JwJdYjtMHgdJRbTVW16cp0vZ9nYBPjJUgyvYx4k/R\nXb9nuxpEtG3c8Ur2pHnzO7UVbZ+WtTREdIGQ+tLd2i4YKGC7+bS1cvKAUnWoZDcuTRE2RVd2jMoU\nIQkqEzB1J7o8yLL4+5p+puvDOnHolNjJBCo8rwUV6pHjcM5npIjsWIvWiY48MZw17SJTSuXzmp/V\nyfs79sekO81H2+FWc5Le3PDH6aXvqa0yCLh5lL5Oc3OTPApAaWFHLTkCDazKA4TmT8XyGaWOE5ho\n3vzcRtllOUDQejvgVPo+DXWSk625nhiKY2pK0TsfTiAzn0kSg1l6jqJ668/JIacyGLR4b2KInda1\nkTaYAN0FsenQ5xJDZKBTIOnr94qhrAGNNVG16uFKTprl5jPOkfQ7V4524sRQtGw1SE3HuSF3byki\nUpdp1UTT6lkZQwIWsoDUJ86JeE0nXwKRJZZC5kSHauGCgJ4dkE9L9659FBds3NA9MRPHOJi/axdB\nhT7ASdq1cpKAQmOj8bv5giWHmmhhEpfnWsPR/HXow3axI115ymJ0/J6AyNXBgclkLAogvD9Fw0kc\nG1DwTOn1mSX2NZ3JlnTIqw7N9vdZwUQPra+7du3UvtY6aJ9ofyW9dzv6eTIRntOhAKK2kvomyUkC\nCpXqIvMaEKnyHUtndk7NvFkPMhw3H6PPdl1cdHBRdGoP68WDzprSqX4cODjHdOxCddl56QqXrmBo\nm3nt6uOuJ2NPDMXpTycfFTSmfuQ81xLTWhOAXF3fJXCpJFBxQYefWZ/3ElBchF2j/BZVmlvKnJTY\nZep7N/jGMDrkEigoIClITY5ASSBBIJna5ahzR6sErtpOnZx0gFJ1uPTv2pFYkLt2TpuuCToOgNxk\nrdOzuz47O3vwK+Jkl2tsNDkw7ZRBgmnXskan4wTQa+VRAcrETlzDnZEkEJqMgstoZ2dnB3/xWVUP\nGEpiDQQTpdpLhjB9r1FW80v10fZzgtX9wjWtQHXZ+lkjd6LYrKNz6snRXX+nPlx6xkVxd3Z9qGWp\nHqY5Klema+MkCpapfmskgTXLWiuPAlAmUHkXcVEkGaOCiRqSAsXSTHiKIsoGmMcSw2FbHHi5aObS\nKqPQlwDpEDMZb9IbJwkdILjh1lowmZhgYlUTwEyMTttEtuUY5nRo/R2bmAIj28+gQV2utSEH+O/q\nZycJKO6dsu86d0IEdlF3ApdWbl9rZ6kjrkV3OjDB6Rjjcsbfnwkqk+4IKL3KxjmjSV+ujakdqV0J\nWPTslqEpbCedi5957eykqg7eY6N6c/VJNktbckPBJSDUNKk/9Zx0lXTudLlWThJQ3E5D1ylOlqie\n5sHVjokhOAPXORAFhbUON3Vmeja1Z+p4Z3w0WgWV/uHlFLncBrbULlc/xw7ooEnvDhCSDiamkvSS\nVtC6jT2fdnZ2tv/HRNd/+jzz4U5hrjKpLAGoS3vMM5QUqNbISQKKSlJwcvj0nDO6foadqcCQHF8n\nY3uitveVOIPUDtb0DUjp/ayOfWib2xgnh9XPjiL3s3SMySFcn7iyJzAjAKuu0nDH7clJzs/83TBh\n2n6gaR0baZ05nejqjw6bVV+pjVpe0hvz0frpUHpqY9eL+6Oo22PkJAFlKcpUrZ/AcmyHeaihOTBx\nuwqV+tIZXedph+kqkTp0GtYtgYm21UVuJ2RFPZTQt+9PYKb6c5LYgkvHz87RdMhDkEtDi2YC7TgE\nmMQiWG4aQrh5E31G+8eBStKhazf7YSpP6+zaqnrWhYak02PkJAFFJUXDpYZOzsi8NE9GMaI+P3fe\nr1+/3r+t33V+d14/22xmjVOmCNr1JmVOTEDbq88y2nIJO+mNkT/pfk0eS0Ciea8BFEZhgjv120Df\nIEQg0b7XZzkE1PRVddDnOv+T+j613fW95kNQOT8/P5iLVPahwVLtcsnW1shJAsoU2RxtZRqH5ERz\nXhNMOr1bUuXKRTujm0x2QsdJRjGxFX1e6a0aOeuRjFlBhEbv0ru2qL6pe9dmpyfXNl4vDXF4n3pk\nXdvJ3Euu6aCO1bnA0UL2mGxP6+R0T30qgDnb0YPPpv51/cHrNfIoAIVCOufuOQNz0dkxFM1HQUT/\nYyUZ8Jo20YH03acc07I9Tg9ad/5NxQRqCpxcqncUmk5AMJ707pxlChyajmmS7hO4kAkxLwUVAmBf\np31GiWUoODNQOKemfngQSLmVQYEvrRx1O11919xbIycJKGslAYszOKWeE0vps0Z/Lqn2zL4aIf9g\nifWcnED/QKqXbF1kXQMqjhVMxlr18L9xOr3ukZl+WuD0R707nfAZ9iXTqg6WmMnEUKhPAoq2XR1V\nhXm5qM9nWtcT49M20167nglMeM0ldgUUF+Ccbaypq8pJAsqxNMuJRsWlKO3KTkxHX8nnjn7GRfNU\nDxdR+ZJjOsVSPvo5tVtBU+caktGv6Rc16HZox2qW2Ior06VxAKrgSoY15eW+V30wDT/TEfUend/l\nVZX/P9nlr4ezIR2SKUiyfYkJar5r5SQBhbK2QROSOqPS7xwVTsqueggozrC1XL2f8mxjcPU4FlAm\nNuM+uw12SWdTHaaIRh1PenGAvlSHxITIFpifi8qdzk2kTqDhPic9pLom/Xc7yLY13w56BAPVwRIr\nnNjikpwkoKRIxM9T1FCEJWBMjqIoX3X44zeXhvsi1kRjtyeB5bKsNZ1MNuPEGWsqY8nReY9RVaNi\nYllpjsaxMzJA9m3S9xpgnOzBTYC64YwrKwEa9co6JEBx6VjX/l5tSVmR62v2y7uCykkCisraRqqB\npfuJnTDvKSoSUHjNfJ0R6H3XVud8S+yEwLDEVFJ9lgxoim7qPDqZ7YaG2iecTFQdtGOQKUxtmtrn\ndKn14LyD9i23CiTnT6yCDMHZqusfd2ZdO18XAPUZbddk88l2luQkAWWJgk3RV69VmXrtnJnlTYzB\nTf5pvVLkWQMors7s5ASaaxkK9TCBmwM0d01jTxPY/QyjPZfk3YoE9UNg0fzoQGyX04tjEnTaqsPh\noaZ3gKJgovNUTu9rg1DqNw1I03OdlvarYM90a+UkAUUlIWYbZaKeVTOQTBRXnbMNQaMlJ7f0Wr9j\nWROgOLCc7iV2scRMpjPFMa8pkun7T/p/nC8uLvYb+TS9zgPoKpr+0pkbs1SPScdcfp+irz7rgNql\ndfoiOHc9+jmCyVpASddp969jTTrUUd0t+VXb+nsHKE5SxElOqp/pfO65lJ/mkyJ4Oqa81SiY/9Qe\n166ldkxgwjq5Nru2p/x4n/TfbRrUd7EsscklHbv68Hl10tSvbCvL02vue+H3rFPS1ySubpOtrslT\ny3dMa62cPKC4aFTlI/XkkETyqnoQNRybcQpWUNKzls1J1ymqkgVMunDXS2ldm5zQ+LQtaVNU1eHE\nKp91f2DWUVOHOW6JVPNZA9L8nsNV6sMxzYnNpKGD6m5K48TpXOuY6sclYZ6TnTJPV2Yf/Xu1R89Q\n1nYGv5+GMP2ZCkpOwk6ZAEXLYrnd+QQD/ayrPinqrIkUa1jKUj4EXoIK2zCBre6H0Od0jsOBSqoX\nl+iXdD6txPDa5ZNsQ+s0MRHNf9J/YhEpUDlQTWCSgp8LuJoXh1Nr5SQBRcU1KDVwYihLz+p3yigc\nqGjdmKczyN3Ob/Di58SqjgED1i2Vt5SPYydcPtVrbd/EEJShdBRUMJlWLyYWoSDI3aTUQ9IRy9LP\nkw5ZF62TA9sl8HDi2q5LxFqebixknZZkySaX5CQBhcZ0DKgwjUNz5s0y+7Mzgok90Qjd7zbcJBl3\npnZ9EwBRH6S+vHesUWge6mB91v9rVqahjMBFdxdF3W+Y2I4pOif2yTYnIHHOrazKzYmwbn3N+mn7\nCLprJAWqBKhdd8eiJ1bj2n9MPVVOElBUFH0n5qGyhqFons5xExvRjqIB8x6BQmkky2I5SdxvSmg8\nDpBTtEzioqH+LzDrTIbS7VfHYpuds6X6TQ6laVr4WxfqxbEs/c7p2Om1r7nJsevS4Du9JjS1QT8n\nYNU0bA+f5byLC6hTAF0jJwkoTqnHgInKhMJ8LkWrpfO7OOxk7NqOdH+JzianT79tcXm4Ors2K5g4\nltTXTJ8mYo+pC9vr2rXUR3pWvWt9+96kv653YlCqhzXtTN9pPbWurq0TQ3H58vN7ASiky9P4uYVG\nocpYS/lTRydGQeNzUbjTciKSwo7va0baZCR9L/24zDEA5qV1SW2rqoPXOEygoPpbwwL4w0hN25O3\n1M9kB64v3LCD+biyXR7UoZbn+oN9NkkCRu66XQNybCvrvCTHgMrJA8rkPEkm49Y8eZ3S0RG6E7tz\nOM5OUXANrddrOjwBj1S2v+/lPl1BoYP1tYuoaxyKeTvjd+LaT1DTAEJAW2JmS+Ul53b5ERxSXvp9\nsgXtw6XVI2dvbkNlAs21oEI9uuBBnSzJSQIK33xW9W4z1U7BdByitXMoVTYNSfOYOlnvaR1dO13E\nJLgQTNrgFAB0C3zSk4KRAxUFT90/Mk2kLvXN1CdVD7fdOyPX9O6zc+gJyAjMjpG4/PSseuRvaljm\npB+nq+4Dt+N2acndyZpA6uq0JCcJKAnBj2loYgNu6KSAwLIcSLg1eoKQpqeRrJXEpNqwOAnI7di6\nBd7pT50ngQnr7ByNxuyM1ekhMQROOroykq6YVwICByY66czhog4xtA2dBzdIsq7HBETNm3pzGzHf\nZRVpLaAw4C7JSQJK+kd73nMd19/TGAgoaYNQ1cN3hWqZU4R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EFMdQJiEqM2K7uRWC0Joy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H6wjJ2dHWuPKYASzCSOoyyuO4739vbmgiWbC2htXC7bUIF0CtPhflKGFrbn+ZxlGUGP0bSSi7bT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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x13699a860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "h = 50\n",
    "w = 37\n",
    "plt.imshow(clf.feature_importances_.reshape((h, w)), cmap=plt.cm.gray)\n",
    "plt.grid(False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tree Ensemble Comparisons"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "%%time\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.ensemble import ExtraTreesClassifier\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "\n",
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "num_estimators = 50\n",
    "# lets train some trees\n",
    "clf_array = [\n",
    "    ('Stump',              DecisionTreeClassifier(max_depth=1, min_samples_leaf=1)),\n",
    "    ('Tree',               DecisionTreeClassifier()),\n",
    "    ('Random Trees',       RandomForestClassifier(max_depth=50, n_estimators=num_estimators)),\n",
    "    ('Extra Random Trees', ExtraTreesClassifier(n_estimators=num_estimators,min_samples_split=2)),\n",
    "    ('Boosted Tree',       GradientBoostingClassifier(n_estimators=num_estimators)), #takes a long time\n",
    "    ]\n",
    "\n",
    "for clf in clf_array:\n",
    "    acc = cross_val_score(clf[1],X,y)\n",
    "    print (clf[0], acc.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pipeline accuracy 0.545815415151\n"
     ]
    },
    {
     "data": {
      "image/png": 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kSZrU2pkl9WfAswcoXx04Z3ThSJKkXjLcPiL7AcdWD6cAd0XEQLte3aG4JElS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\nSVJtTEQkSVJtTEQkSVJt/j8U8k3smBPHKwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1368a1630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# from sklearn.svm import SVC\n",
    "# from sklearn.linear_model import LogisticRegression\n",
    "# from sklearn.linear_model import SGDClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "\n",
    "\n",
    "# setup pipeline to take PCA, then fit a different classifier\n",
    "clf_pipe = Pipeline(\n",
    "    [('PCA',PCA(n_components=100,svd_solver='randomized')),\n",
    "     ('CLF',GaussianNB())]\n",
    ")\n",
    "\n",
    "# now iterate through and get predictions, saved to the correct row in yhat\n",
    "for train, test in cv.split(X,y):\n",
    "    clf_pipe.fit(X[train],y[train])\n",
    "    yhat[test] = clf_pipe.predict(X[test])\n",
    "\n",
    "total_accuracy = mt.accuracy_score(y, yhat)\n",
    "print ('Pipeline accuracy', total_accuracy)\n",
    "plot_class_acc(y,yhat,title=\"Naive Bayes + PCA\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Accuracy: 0.545815415151\n",
      "Number of infrequent faces: 43 with average recall of: 0.399742710544\n",
      "Number of frequent faces: 19 with average precision of: 0.557294021993\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics as mt\n",
    "\n",
    "freq_infreq_threshold = 40\n",
    "\n",
    "# get various measures of performance\n",
    "total_accuracy = mt.accuracy_score(y, yhat)\n",
    "per_class_acc_list = per_class_accuracy(y,yhat)\n",
    "\n",
    "prec_for_freq_classes = []\n",
    "recall_for_infreq_classes = []\n",
    "rec_tot = []\n",
    "prec_tot = []\n",
    "\n",
    "for cls in np.unique(y):\n",
    "    idx = (y==cls) # get classes\n",
    "    ytmp_actual = np.zeros(y.shape) # make binary class problem\n",
    "    ytmp_actual[idx] = 1 # set the instances for this specific class\n",
    "    \n",
    "    ytmp_predicted = np.zeros(y.shape) # binary prediction array\n",
    "    ytmp_predicted[yhat==cls] = 1\n",
    "    \n",
    "    num_in_class = sum(idx)\n",
    "    \n",
    "    rec = mt.recall_score(ytmp_actual, ytmp_predicted)\n",
    "    prec = mt.precision_score(ytmp_actual, ytmp_predicted)\n",
    "    rec_tot.append(rec)\n",
    "    prec_tot.append(prec)\n",
    "    \n",
    "    if num_in_class < freq_infreq_threshold:\n",
    "        recall_for_infreq_classes.append(rec)\n",
    "    elif num_in_class >= freq_infreq_threshold:\n",
    "        prec_for_freq_classes.append(prec)\n",
    "        \n",
    "print ('Total Accuracy:',total_accuracy)\n",
    "print ('Number of infrequent faces:',len(recall_for_infreq_classes), \n",
    "       'with average recall of:', np.mean(recall_for_infreq_classes))\n",
    "print ('Number of frequent faces:',len(prec_for_freq_classes), \n",
    "       'with average precision of:',np.mean(prec_for_freq_classes))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ZWlrO6mo/X4OFhWFuvXV28vMg+NpMgi8AAACYsr1713L99f0MfWZmkvn5ZHFxLSsr/XwN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\n/2FUPvSG0MIYS1D/sHs6eB1Dxr7MvD0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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11dab3908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# But we can really summarize this data much better than this. \n",
    "# How about looking at more statistics of the precision and recall for each class?\n",
    "\n",
    "import seaborn as sns\n",
    "sns.set_palette(\"dark\")\n",
    "plt.figure(figsize=(15,5))\n",
    "plt.boxplot([ rec_tot, prec_tot, recall_for_infreq_classes,prec_for_freq_classes],\n",
    "            labels=['Recall, all faces','Precision, all faces','Recall Infreq. faces','Prec Freq. faces'])\n",
    "plt.ylim([0,1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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AeAHAkwAeMsb8bnDcwwDuB3AvgCcAPGCMeWqQFK+czqGNgXKs/OjmzaGt/3Kq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\ndyoLwBACs+s1qucN+np7JCn1Pu+ho/LtwkYd69q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      "text/plain": [
       "<matplotlib.figure.Figure at 0x133f7c4a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# how about plotting a confusion matrix?\n",
    "cm = mt.confusion_matrix(y, yhat)\n",
    "plt.imshow(cm,cmap=plt.get_cmap('Reds'),aspect='auto')\n",
    "plt.grid(False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# What is the problem with this graph??"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# %load -r 32-34 statcompare.py\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pipeline accuracy 0.545484617929\n"
     ]
    }
   ],
   "source": [
    "from sklearn.naive_bayes import GaussianNB\n",
    "\n",
    "\n",
    "# setup pipeline to take PCA, then fit a different classifier\n",
    "clf_pipe = Pipeline(\n",
    "    [('PCA',PCA(n_components=100,svd_solver='randomized')),\n",
    "     ('CLF',GaussianNB())]\n",
    ")\n",
    "\n",
    "yhat_score = np.zeros((y.shape[0],len(lfw_people.target_names)))\n",
    "\n",
    "# now iterate through and get predictions, saved to the correct row in yhat\n",
    "for train, test in cv.split(X,y):\n",
    "    clf_pipe.fit(X[train],y[train])\n",
    "    yhat[test] = clf_pipe.predict(X[test])\n",
    "    yhat_score[test] = clf_pipe.predict_proba(X[test])\n",
    "\n",
    "total_accuracy = mt.accuracy_score(y, yhat)\n",
    "print ('Pipeline accuracy', total_accuracy)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using the ROC Curve\n",
    "In the below example we can see how to graph the false negatives and false positives in an ROC curve for a given classifier. Please note that the \"scores\" from the classifier have already been populated using the code above. The scores can be interpretted as the the probability that a given class should be designated as positive. These scores are needed so that the ROC can change thresholds deciding if theclass is positive or negative. Once we have the scores, we can send each column of scores (i.e., the probability for that class being positive) into the ROC curve generator and it will give use the arrays of false positives and negatives for that class as the threshold is increased. \n",
    "\n",
    "We save the outputs into a dictionary of fpr and tpr (false positive and true positive rates). The keys to the dictionary are the class value. We can also compute the ROC treshold for all the classes by placing their probabilities into one giant vector and whether they should or should not be a value of one. Please note that this method of combining all classes into a single ROC is not considered a standard method--it has limited utility outside classifier comparison. If you understand the limitations it can be an effective tool. If not, then I would not recommend using it to compare models right away. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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MCvVBRKI9koSkBVK7dZFkpIX8ZU4YV8j8Me3f+BXDiU89/hrvyt7N/PV3A7B8wmIGxjfe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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x110ff8320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "sns.set_palette(\"dark\")\n",
    "# code manipulated from http://scikit-learn.org/stable/auto_examples/plot_roc.html\n",
    "from sklearn.preprocessing import label_binarize\n",
    "\n",
    "# Compute ROC curve for a subset of interesting classes\n",
    "fpr = dict()\n",
    "tpr = dict()\n",
    "roc_auc = dict()\n",
    "for i in np.unique(y):\n",
    "    fpr[i], tpr[i], _ = mt.roc_curve(y, yhat_score[:, i], pos_label=i)\n",
    "    roc_auc[i] = mt.auc(fpr[i], tpr[i])\n",
    "\n",
    "for i in np.random.permutation(60)[0:6]:\n",
    "    plt.plot(fpr[i], tpr[i], label='class {0} with {1} instances (area = {2:0.2f})'\n",
    "                                   ''.format(i, sum(y==i), roc_auc[i]))\n",
    "\n",
    "plt.legend(loc=\"lower right\")  \n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x124c2e2e8>]"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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SAzCAT5D3fPRH/NePl+117u/+7BVcfeXpdapIkiRJkjSRDODjbEfPAO/88C38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\nmgAGcEmSJEmSJsD/B0sZ/P+EiwToAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1110e4be0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# combined ROC over all classes\n",
    "one_hot_class_encoding = label_binarize(y,np.unique(y))\n",
    "fpr[\"micro\"], tpr[\"micro\"], _ = mt.roc_curve(one_hot_class_encoding.ravel(), yhat_score.ravel())\n",
    "roc_auc[\"micro\"] = mt.auc(fpr[\"micro\"], tpr[\"micro\"])\n",
    "\n",
    "plt.figure(figsize=(12,4))\n",
    "plt.plot(fpr[\"micro\"], tpr[\"micro\"],\n",
    "         label='micro-average ROC curve (area = {0:0.2f})'\n",
    "               ''.format(roc_auc[\"micro\"]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "# Statistical Comparison \n",
    "- how might you compare two classifiers trained on the exact same datasets?\n",
    "\n",
    "Note: if these cells are not loaded, you might need to run the `%load ` magic."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# %load -r 1-15 statcompare.py\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# %load -r 19-28 statcompare.py\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
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